step_to parameter in predict function

docs/build/html/_static/websupport.js

@@ -1,808 +0,0 @@
-/*
- * websupport.js
- * ~~~~~~~~~~~~~
- *
- * sphinx.websupport utilities for all documentation.
- *
- * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-(function($) {
-  $.fn.autogrow = function() {
-    return this.each(function() {
-    var textarea = this;
-
-    $.fn.autogrow.resize(textarea);
-
-    $(textarea)
-      .focus(function() {
-        textarea.interval = setInterval(function() {
-          $.fn.autogrow.resize(textarea);
-        }, 500);
-      })
-      .blur(function() {
-        clearInterval(textarea.interval);
-      });
-    });
-  };
-
-  $.fn.autogrow.resize = function(textarea) {
-    var lineHeight = parseInt($(textarea).css('line-height'), 10);
-    var lines = textarea.value.split('\n');
-    var columns = textarea.cols;
-    var lineCount = 0;
-    $.each(lines, function() {
-      lineCount += Math.ceil(this.length / columns) || 1;
-    });
-    var height = lineHeight * (lineCount + 1);
-    $(textarea).css('height', height);
-  };
-})(jQuery);
-
-(function($) {
-  var comp, by;
-
-  function init() {
-    initEvents();
-    initComparator();
-  }
-
-  function initEvents() {
-    $(document).on("click", 'a.comment-close', function(event) {
-      event.preventDefault();
-      hide($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.vote', function(event) {
-      event.preventDefault();
-      handleVote($(this));
-    });
-    $(document).on("click", 'a.reply', function(event) {
-      event.preventDefault();
-      openReply($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.close-reply', function(event) {
-      event.preventDefault();
-      closeReply($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.sort-option', function(event) {
-      event.preventDefault();
-      handleReSort($(this));
-    });
-    $(document).on("click", 'a.show-proposal', function(event) {
-      event.preventDefault();
-      showProposal($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.hide-proposal', function(event) {
-      event.preventDefault();
-      hideProposal($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.show-propose-change', function(event) {
-      event.preventDefault();
-      showProposeChange($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.hide-propose-change', function(event) {
-      event.preventDefault();
-      hideProposeChange($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.accept-comment', function(event) {
-      event.preventDefault();
-      acceptComment($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.delete-comment', function(event) {
-      event.preventDefault();
-      deleteComment($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.comment-markup', function(event) {
-      event.preventDefault();
-      toggleCommentMarkupBox($(this).attr('id').substring(2));
-    });
-  }
-
-  /**
-   * Set comp, which is a comparator function used for sorting and
-   * inserting comments into the list.
-   */
-  function setComparator() {
-    // If the first three letters are "asc", sort in ascending order
-    // and remove the prefix.
-    if (by.substring(0,3) == 'asc') {
-      var i = by.substring(3);
-      comp = function(a, b) { return a[i] - b[i]; };
-    } else {
-      // Otherwise sort in descending order.
-      comp = function(a, b) { return b[by] - a[by]; };
-    }
-
-    // Reset link styles and format the selected sort option.
-    $('a.sel').attr('href', '#').removeClass('sel');
-    $('a.by' + by).removeAttr('href').addClass('sel');
-  }
-
-  /**
-   * Create a comp function. If the user has preferences stored in
-   * the sortBy cookie, use those, otherwise use the default.
-   */
-  function initComparator() {
-    by = 'rating'; // Default to sort by rating.
-    // If the sortBy cookie is set, use that instead.
-    if (document.cookie.length > 0) {
-      var start = document.cookie.indexOf('sortBy=');
-      if (start != -1) {
-        start = start + 7;
-        var end = document.cookie.indexOf(";", start);
-        if (end == -1) {
-          end = document.cookie.length;
-          by = unescape(document.cookie.substring(start, end));
-        }
-      }
-    }
-    setComparator();
-  }
-
-  /**
-   * Show a comment div.
-   */
-  function show(id) {
-    $('#ao' + id).hide();
-    $('#ah' + id).show();
-    var context = $.extend({id: id}, opts);
-    var popup = $(renderTemplate(popupTemplate, context)).hide();
-    popup.find('textarea[name="proposal"]').hide();
-    popup.find('a.by' + by).addClass('sel');
-    var form = popup.find('#cf' + id);
-    form.submit(function(event) {
-      event.preventDefault();
-      addComment(form);
-    });
-    $('#s' + id).after(popup);
-    popup.slideDown('fast', function() {
-      getComments(id);
-    });
-  }
-
-  /**
-   * Hide a comment div.
-   */
-  function hide(id) {
-    $('#ah' + id).hide();
-    $('#ao' + id).show();
-    var div = $('#sc' + id);
-    div.slideUp('fast', function() {
-      div.remove();
-    });
-  }
-
-  /**
-   * Perform an ajax request to get comments for a node
-   * and insert the comments into the comments tree.
-   */
-  function getComments(id) {
-    $.ajax({
-     type: 'GET',
-     url: opts.getCommentsURL,
-     data: {node: id},
-     success: function(data, textStatus, request) {
-       var ul = $('#cl' + id);
-       var speed = 100;
-       $('#cf' + id)
-         .find('textarea[name="proposal"]')
-         .data('source', data.source);
-
-       if (data.comments.length === 0) {
-         ul.html('<li>No comments yet.</li>');
-         ul.data('empty', true);
-       } else {
-         // If there are comments, sort them and put them in the list.
-         var comments = sortComments(data.comments);
-         speed = data.comments.length * 100;
-         appendComments(comments, ul);
-         ul.data('empty', false);
-       }
-       $('#cn' + id).slideUp(speed + 200);
-       ul.slideDown(speed);
-     },
-     error: function(request, textStatus, error) {
-       showError('Oops, there was a problem retrieving the comments.');
-     },
-     dataType: 'json'
-    });
-  }
-
-  /**
-   * Add a comment via ajax and insert the comment into the comment tree.
-   */
-  function addComment(form) {
-    var node_id = form.find('input[name="node"]').val();
-    var parent_id = form.find('input[name="parent"]').val();
-    var text = form.find('textarea[name="comment"]').val();
-    var proposal = form.find('textarea[name="proposal"]').val();
-
-    if (text == '') {
-      showError('Please enter a comment.');
-      return;
-    }
-
-    // Disable the form that is being submitted.
-    form.find('textarea,input').attr('disabled', 'disabled');
-
-    // Send the comment to the server.
-    $.ajax({
-      type: "POST",
-      url: opts.addCommentURL,
-      dataType: 'json',
-      data: {
-        node: node_id,
-        parent: parent_id,
-        text: text,
-        proposal: proposal
-      },
-      success: function(data, textStatus, error) {
-        // Reset the form.
-        if (node_id) {
-          hideProposeChange(node_id);
-        }
-        form.find('textarea')
-          .val('')
-          .add(form.find('input'))
-          .removeAttr('disabled');
-	var ul = $('#cl' + (node_id || parent_id));
-        if (ul.data('empty')) {
-          $(ul).empty();
-          ul.data('empty', false);
-        }
-        insertComment(data.comment);
-        var ao = $('#ao' + node_id);
-        ao.find('img').attr({'src': opts.commentBrightImage});
-        if (node_id) {
-          // if this was a "root" comment, remove the commenting box
-          // (the user can get it back by reopening the comment popup)
-          $('#ca' + node_id).slideUp();
-        }
-      },
-      error: function(request, textStatus, error) {
-        form.find('textarea,input').removeAttr('disabled');
-        showError('Oops, there was a problem adding the comment.');
-      }
-    });
-  }
-
-  /**
-   * Recursively append comments to the main comment list and children
-   * lists, creating the comment tree.
-   */
-  function appendComments(comments, ul) {
-    $.each(comments, function() {
-      var div = createCommentDiv(this);
-      ul.append($(document.createElement('li')).html(div));
-      appendComments(this.children, div.find('ul.comment-children'));
-      // To avoid stagnating data, don't store the comments children in data.
-      this.children = null;
-      div.data('comment', this);
-    });
-  }
-
-  /**
-   * After adding a new comment, it must be inserted in the correct
-   * location in the comment tree.
-   */
-  function insertComment(comment) {
-    var div = createCommentDiv(comment);
-
-    // To avoid stagnating data, don't store the comments children in data.
-    comment.children = null;
-    div.data('comment', comment);
-
-    var ul = $('#cl' + (comment.node || comment.parent));
-    var siblings = getChildren(ul);
-
-    var li = $(document.createElement('li'));
-    li.hide();
-
-    // Determine where in the parents children list to insert this comment.
-    for(var i=0; i < siblings.length; i++) {
-      if (comp(comment, siblings[i]) <= 0) {
-        $('#cd' + siblings[i].id)
-          .parent()
-          .before(li.html(div));
-        li.slideDown('fast');
-        return;
-      }
-    }
-
-    // If we get here, this comment rates lower than all the others,
-    // or it is the only comment in the list.
-    ul.append(li.html(div));
-    li.slideDown('fast');
-  }
-
-  function acceptComment(id) {
-    $.ajax({
-      type: 'POST',
-      url: opts.acceptCommentURL,
-      data: {id: id},
-      success: function(data, textStatus, request) {
-        $('#cm' + id).fadeOut('fast');
-        $('#cd' + id).removeClass('moderate');
-      },
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem accepting the comment.');
-      }
-    });
-  }
-
-  function deleteComment(id) {
-    $.ajax({
-      type: 'POST',
-      url: opts.deleteCommentURL,
-      data: {id: id},
-      success: function(data, textStatus, request) {
-        var div = $('#cd' + id);
-        if (data == 'delete') {
-          // Moderator mode: remove the comment and all children immediately
-          div.slideUp('fast', function() {
-            div.remove();
-          });
-          return;
-        }
-        // User mode: only mark the comment as deleted
-        div
-          .find('span.user-id:first')
-          .text('[deleted]').end()
-          .find('div.comment-text:first')
-          .text('[deleted]').end()
-          .find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
-                ', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
-          .remove();
-        var comment = div.data('comment');
-        comment.username = '[deleted]';
-        comment.text = '[deleted]';
-        div.data('comment', comment);
-      },
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem deleting the comment.');
-      }
-    });
-  }
-
-  function showProposal(id) {
-    $('#sp' + id).hide();
-    $('#hp' + id).show();
-    $('#pr' + id).slideDown('fast');
-  }
-
-  function hideProposal(id) {
-    $('#hp' + id).hide();
-    $('#sp' + id).show();
-    $('#pr' + id).slideUp('fast');
-  }
-
-  function showProposeChange(id) {
-    $('#pc' + id).hide();
-    $('#hc' + id).show();
-    var textarea = $('#pt' + id);
-    textarea.val(textarea.data('source'));
-    $.fn.autogrow.resize(textarea[0]);
-    textarea.slideDown('fast');
-  }
-
-  function hideProposeChange(id) {
-    $('#hc' + id).hide();
-    $('#pc' + id).show();
-    var textarea = $('#pt' + id);
-    textarea.val('').removeAttr('disabled');
-    textarea.slideUp('fast');
-  }
-
-  function toggleCommentMarkupBox(id) {
-    $('#mb' + id).toggle();
-  }
-
-  /** Handle when the user clicks on a sort by link. */
-  function handleReSort(link) {
-    var classes = link.attr('class').split(/\s+/);
-    for (var i=0; i<classes.length; i++) {
-      if (classes[i] != 'sort-option') {
-	by = classes[i].substring(2);
-      }
-    }
-    setComparator();
-    // Save/update the sortBy cookie.
-    var expiration = new Date();
-    expiration.setDate(expiration.getDate() + 365);
-    document.cookie= 'sortBy=' + escape(by) +
-                     ';expires=' + expiration.toUTCString();
-    $('ul.comment-ul').each(function(index, ul) {
-      var comments = getChildren($(ul), true);
-      comments = sortComments(comments);
-      appendComments(comments, $(ul).empty());
-    });
-  }
-
-  /**
-   * Function to process a vote when a user clicks an arrow.
-   */
-  function handleVote(link) {
-    if (!opts.voting) {
-      showError("You'll need to login to vote.");
-      return;
-    }
-
-    var id = link.attr('id');
-    if (!id) {
-      // Didn't click on one of the voting arrows.
-      return;
-    }
-    // If it is an unvote, the new vote value is 0,
-    // Otherwise it's 1 for an upvote, or -1 for a downvote.
-    var value = 0;
-    if (id.charAt(1) != 'u') {
-      value = id.charAt(0) == 'u' ? 1 : -1;
-    }
-    // The data to be sent to the server.
-    var d = {
-      comment_id: id.substring(2),
-      value: value
-    };
-
-    // Swap the vote and unvote links.
-    link.hide();
-    $('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
-      .show();
-
-    // The div the comment is displayed in.
-    var div = $('div#cd' + d.comment_id);
-    var data = div.data('comment');
-
-    // If this is not an unvote, and the other vote arrow has
-    // already been pressed, unpress it.
-    if ((d.value !== 0) && (data.vote === d.value * -1)) {
-      $('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
-      $('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
-    }
-
-    // Update the comments rating in the local data.
-    data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
-    data.vote = d.value;
-    div.data('comment', data);
-
-    // Change the rating text.
-    div.find('.rating:first')
-      .text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
-
-    // Send the vote information to the server.
-    $.ajax({
-      type: "POST",
-      url: opts.processVoteURL,
-      data: d,
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem casting that vote.');
-      }
-    });
-  }
-
-  /**
-   * Open a reply form used to reply to an existing comment.
-   */
-  function openReply(id) {
-    // Swap out the reply link for the hide link
-    $('#rl' + id).hide();
-    $('#cr' + id).show();
-
-    // Add the reply li to the children ul.
-    var div = $(renderTemplate(replyTemplate, {id: id})).hide();
-    $('#cl' + id)
-      .prepend(div)
-      // Setup the submit handler for the reply form.
-      .find('#rf' + id)
-      .submit(function(event) {
-        event.preventDefault();
-        addComment($('#rf' + id));
-        closeReply(id);
-      })
-      .find('input[type=button]')
-      .click(function() {
-        closeReply(id);
-      });
-    div.slideDown('fast', function() {
-      $('#rf' + id).find('textarea').focus();
-    });
-  }
-
-  /**
-   * Close the reply form opened with openReply.
-   */
-  function closeReply(id) {
-    // Remove the reply div from the DOM.
-    $('#rd' + id).slideUp('fast', function() {
-      $(this).remove();
-    });
-
-    // Swap out the hide link for the reply link
-    $('#cr' + id).hide();
-    $('#rl' + id).show();
-  }
-
-  /**
-   * Recursively sort a tree of comments using the comp comparator.
-   */
-  function sortComments(comments) {
-    comments.sort(comp);
-    $.each(comments, function() {
-      this.children = sortComments(this.children);
-    });
-    return comments;
-  }
-
-  /**
-   * Get the children comments from a ul. If recursive is true,
-   * recursively include childrens' children.
-   */
-  function getChildren(ul, recursive) {
-    var children = [];
-    ul.children().children("[id^='cd']")
-      .each(function() {
-        var comment = $(this).data('comment');
-        if (recursive)
-          comment.children = getChildren($(this).find('#cl' + comment.id), true);
-        children.push(comment);
-      });
-    return children;
-  }
-
-  /** Create a div to display a comment in. */
-  function createCommentDiv(comment) {
-    if (!comment.displayed && !opts.moderator) {
-      return $('<div class="moderate">Thank you!  Your comment will show up '
-               + 'once it is has been approved by a moderator.</div>');
-    }
-    // Prettify the comment rating.
-    comment.pretty_rating = comment.rating + ' point' +
-      (comment.rating == 1 ? '' : 's');
-    // Make a class (for displaying not yet moderated comments differently)
-    comment.css_class = comment.displayed ? '' : ' moderate';
-    // Create a div for this comment.
-    var context = $.extend({}, opts, comment);
-    var div = $(renderTemplate(commentTemplate, context));
-
-    // If the user has voted on this comment, highlight the correct arrow.
-    if (comment.vote) {
-      var direction = (comment.vote == 1) ? 'u' : 'd';
-      div.find('#' + direction + 'v' + comment.id).hide();
-      div.find('#' + direction + 'u' + comment.id).show();
-    }
-
-    if (opts.moderator || comment.text != '[deleted]') {
-      div.find('a.reply').show();
-      if (comment.proposal_diff)
-        div.find('#sp' + comment.id).show();
-      if (opts.moderator && !comment.displayed)
-        div.find('#cm' + comment.id).show();
-      if (opts.moderator || (opts.username == comment.username))
-        div.find('#dc' + comment.id).show();
-    }
-    return div;
-  }
-
-  /**
-   * A simple template renderer. Placeholders such as <%id%> are replaced
-   * by context['id'] with items being escaped. Placeholders such as <#id#>
-   * are not escaped.
-   */
-  function renderTemplate(template, context) {
-    var esc = $(document.createElement('div'));
-
-    function handle(ph, escape) {
-      var cur = context;
-      $.each(ph.split('.'), function() {
-        cur = cur[this];
-      });
-      return escape ? esc.text(cur || "").html() : cur;
-    }
-
-    return template.replace(/<([%#])([\w\.]*)\1>/g, function() {
-      return handle(arguments[2], arguments[1] == '%' ? true : false);
-    });
-  }
-
-  /** Flash an error message briefly. */
-  function showError(message) {
-    $(document.createElement('div')).attr({'class': 'popup-error'})
-      .append($(document.createElement('div'))
-               .attr({'class': 'error-message'}).text(message))
-      .appendTo('body')
-      .fadeIn("slow")
-      .delay(2000)
-      .fadeOut("slow");
-  }
-
-  /** Add a link the user uses to open the comments popup. */
-  $.fn.comment = function() {
-    return this.each(function() {
-      var id = $(this).attr('id').substring(1);
-      var count = COMMENT_METADATA[id];
-      var title = count + ' comment' + (count == 1 ? '' : 's');
-      var image = count > 0 ? opts.commentBrightImage : opts.commentImage;
-      var addcls = count == 0 ? ' nocomment' : '';
-      $(this)
-        .append(
-          $(document.createElement('a')).attr({
-            href: '#',
-            'class': 'sphinx-comment-open' + addcls,
-            id: 'ao' + id
-          })
-            .append($(document.createElement('img')).attr({
-              src: image,
-              alt: 'comment',
-              title: title
-            }))
-            .click(function(event) {
-              event.preventDefault();
-              show($(this).attr('id').substring(2));
-            })
-        )
-        .append(
-          $(document.createElement('a')).attr({
-            href: '#',
-            'class': 'sphinx-comment-close hidden',
-            id: 'ah' + id
-          })
-            .append($(document.createElement('img')).attr({
-              src: opts.closeCommentImage,
-              alt: 'close',
-              title: 'close'
-            }))
-            .click(function(event) {
-              event.preventDefault();
-              hide($(this).attr('id').substring(2));
-            })
-        );
-    });
-  };
-
-  var opts = {
-    processVoteURL: '/_process_vote',
-    addCommentURL: '/_add_comment',
-    getCommentsURL: '/_get_comments',
-    acceptCommentURL: '/_accept_comment',
-    deleteCommentURL: '/_delete_comment',
-    commentImage: '/static/_static/comment.png',
-    closeCommentImage: '/static/_static/comment-close.png',
-    loadingImage: '/static/_static/ajax-loader.gif',
-    commentBrightImage: '/static/_static/comment-bright.png',
-    upArrow: '/static/_static/up.png',
-    downArrow: '/static/_static/down.png',
-    upArrowPressed: '/static/_static/up-pressed.png',
-    downArrowPressed: '/static/_static/down-pressed.png',
-    voting: false,
-    moderator: false
-  };
-
-  if (typeof COMMENT_OPTIONS != "undefined") {
-    opts = jQuery.extend(opts, COMMENT_OPTIONS);
-  }
-
-  var popupTemplate = '\
-    <div class="sphinx-comments" id="sc<%id%>">\
-      <p class="sort-options">\
-        Sort by:\
-        <a href="#" class="sort-option byrating">best rated</a>\
-        <a href="#" class="sort-option byascage">newest</a>\
-        <a href="#" class="sort-option byage">oldest</a>\
-      </p>\
-      <div class="comment-header">Comments</div>\
-      <div class="comment-loading" id="cn<%id%>">\
-        loading comments... <img src="<%loadingImage%>" alt="" /></div>\
-      <ul id="cl<%id%>" class="comment-ul"></ul>\
-      <div id="ca<%id%>">\
-      <p class="add-a-comment">Add a comment\
-        (<a href="#" class="comment-markup" id="ab<%id%>">markup</a>):</p>\
-      <div class="comment-markup-box" id="mb<%id%>">\
-        reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
-        <code>``code``</code>, \
-        code blocks: <code>::</code> and an indented block after blank line</div>\
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-            Propose a change &#9657;\
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-            Propose a change &#9663;\
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-            <img src="<%upArrowPressed%>" />\
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-        </div>\
-        <div class="arrow">\
-          <a href="#" id="dv<%id%>" class="vote" title="vote down">\
-            <img src="<%downArrow%>" id="da<%id%>" />\
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-          <a href="#" id="du<%id%>" class="un vote" title="vote down">\
-            <img src="<%downArrowPressed%>" />\
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-          <span id="cm<%id%>" class="moderation hidden">\
-            <a href="#" id="ac<%id%>" class="accept-comment">accept</a>\
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-        <pre class="proposal" id="pr<%id%>">\
-<#proposal_diff#>\
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-
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-          <input type="button" value="Cancel" />\
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-});

docs/build/html/genindex.html

@@ -772,6 +772,8 @@
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.forecast_step">forecast_step() (pyFTS.common.fts.FTS method)</a>
+</li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">format_data() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 
       <ul>
@@ -2014,6 +2016,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.predict">predict() (pyFTS.common.fts.FTS method)</a>
 </li>
       <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.prefix">prefix (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
+</li>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Grid.PreFixedGridPartitioner">PreFixedGridPartitioner (class in pyFTS.partitioners.Grid)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_distribution_statistics">print_distribution_statistics() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>

docs/build/html/pyFTS.benchmarks.html

@@ -1400,7 +1400,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1418,7 +1418,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/arima.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1516,7 +1516,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1534,7 +1534,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/knn.html#KNearestNeighbors.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1659,7 +1659,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1677,7 +1677,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/quantreg.html#QuantileRegression.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1788,7 +1788,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1806,7 +1806,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1824,7 +1824,7 @@ of the metric ‘measure’ with the same ‘tag’, returning a Pandas DataFram
 <dl class="py method">
 <dt id="pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/benchmarks/BSTS.html#ARIMA.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.common.html

@@ -150,7 +150,7 @@
 <dl class="py class">
 <dt id="pyFTS.common.FLR.FLR">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.FLR.</code><code class="sig-name descname">FLR</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">LHS</span></em>, <em class="sig-param"><span class="n">RHS</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/FLR.html#FLR"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.FLR.FLR" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Fuzzy Logical Relationship</p>
 <p>Represents a temporal transition of the fuzzy set LHS on time t for the fuzzy set RHS on time t+1.</p>
 <dl class="py attribute">
@@ -246,7 +246,7 @@
 <dl class="py class">
 <dt id="pyFTS.common.FuzzySet.FuzzySet">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.FuzzySet.</code><code class="sig-name descname">FuzzySet</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em>, <em class="sig-param"><span class="n">mf</span></em>, <em class="sig-param"><span class="n">parameters</span></em>, <em class="sig-param"><span class="n">centroid</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/FuzzySet.html#FuzzySet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.FuzzySet.FuzzySet" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Fuzzy Set</p>
 <dl class="py attribute">
 <dt id="pyFTS.common.FuzzySet.FuzzySet.Z">
@@ -600,7 +600,7 @@
 <dl class="py class">
 <dt id="pyFTS.common.SortedCollection.SortedCollection">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.SortedCollection.</code><code class="sig-name descname">SortedCollection</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">iterable</span><span class="o">=</span><span class="default_value">()</span></em>, <em class="sig-param"><span class="n">key</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/SortedCollection.html#SortedCollection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.SortedCollection.SortedCollection" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Sequence sorted by a key function.</p>
 <p>SortedCollection() is much easier to work with than using bisect() directly.
 It supports key functions like those use in sorted(), min(), and max().
@@ -1096,7 +1096,7 @@ y(t) = ( y(t-1) * y’(t) ) + y(t-1)</p>
 <dl class="py class">
 <dt id="pyFTS.common.Transformations.Transformation">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.Transformations.</code><code class="sig-name descname">Transformation</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/Transformations.html#Transformation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.Transformations.Transformation" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Data transformation used on pre and post processing of the FTS</p>
 <dl class="py method">
 <dt id="pyFTS.common.Transformations.Transformation.apply">
@@ -1413,7 +1413,7 @@ y(t) = ( y(t-1) * y’(t) ) + y(t-1)</p>
 <dl class="py class">
 <dt id="pyFTS.common.flrg.FLRG">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.flrg.</code><code class="sig-name descname">FLRG</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/flrg.html#FLRG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.flrg.FLRG" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Fuzzy Logical Relationship Group</p>
 <p>Group a set of FLR’s with the same LHS. Represents the temporal patterns for time t+1 (the RHS fuzzy sets)
 when the LHS pattern is identified on time t.</p>
@@ -1523,7 +1523,7 @@ when the LHS pattern is identified on time t.</p>
 <dl class="py class">
 <dt id="pyFTS.common.fts.FTS">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.fts.</code><code class="sig-name descname">FTS</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Fuzzy Time Series object model</p>
 <dl class="py attribute">
 <dt id="pyFTS.common.fts.FTS.alpha_cut">
@@ -1673,7 +1673,7 @@ when the LHS pattern is identified on time t.</p>
 <dl class="py method">
 <dt id="pyFTS.common.fts.FTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1691,7 +1691,7 @@ when the LHS pattern is identified on time t.</p>
 <dl class="py method">
 <dt id="pyFTS.common.fts.FTS.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1709,7 +1709,7 @@ when the LHS pattern is identified on time t.</p>
 <dl class="py method">
 <dt id="pyFTS.common.fts.FTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1793,6 +1793,24 @@ when the LHS pattern is identified on time t.</p>
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt id="pyFTS.common.fts.FTS.forecast_step">
+<code class="sig-name descname">forecast_step</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">step</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.forecast_step"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.forecast_step" title="Permalink to this definition">¶</a></dt>
+<dd><p>Point forecast for H steps ahead, where H is given by the step parameter</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</p></li>
+<li><p><strong>step</strong> – the forecasting horizon (default: 1)</p></li>
+<li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list with the forecasted values</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt id="pyFTS.common.fts.FTS.fuzzy">
 <code class="sig-name descname">fuzzy</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/fts.html#FTS.fuzzy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.fts.FTS.fuzzy" title="Permalink to this definition">¶</a></dt>
@@ -1981,7 +1999,8 @@ model, among other parameters.</p>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>data</strong> – time series with minimal length to the order of the model</p></li>
 <li><p><strong>type</strong> – the forecasting type, one of these values: point(default), interval, distribution or multivariate.</p></li>
-<li><p><strong>steps_ahead</strong> – The forecasting horizon, i. e., the number of steps ahead to forecast (default value: 1)</p></li>
+<li><p><strong>steps_ahead</strong> – The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.</p></li>
+<li><p><strong>step_to</strong> – The forecasting step H, i. e., tell the model to forecast to t+H for each input sample</p></li>
 <li><p><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default value: 0)</p></li>
 <li><p><strong>distributed</strong> – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)</p></li>
 <li><p><strong>nodes</strong> – a list with the dispy cluster nodes addresses</p></li>
@@ -2056,14 +2075,14 @@ models that accept the actual values and forecast new ones.</p></li>
 <dl class="py class">
 <dt id="pyFTS.common.tree.FLRGTree">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.tree.</code><code class="sig-name descname">FLRGTree</code><a class="reference internal" href="_modules/pyFTS/common/tree.html#FLRGTree"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.tree.FLRGTree" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Represents a FLRG set with a tree structure</p>
 </dd></dl>
 
 <dl class="py class">
 <dt id="pyFTS.common.tree.FLRGTreeNode">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.common.tree.</code><code class="sig-name descname">FLRGTreeNode</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/common/tree.html#FLRGTreeNode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.common.tree.FLRGTreeNode" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Tree node for</p>
 <dl class="py method">
 <dt id="pyFTS.common.tree.FLRGTreeNode.appendChild">

docs/build/html/pyFTS.data.html

@@ -106,7 +106,7 @@ If the file don’t already exists, it will be downloaded and decompressed.</p>
 <dl class="py class">
 <dt id="pyFTS.data.artificial.SignalEmulator">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.data.artificial.</code><code class="sig-name descname">SignalEmulator</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Emulate a complex signal built from several additive and non-additive components</p>
 <dl class="py method">
 <dt id="pyFTS.data.artificial.SignalEmulator.blip">

docs/build/html/pyFTS.models.ensemble.html

@@ -143,7 +143,7 @@ XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Bra
 <dl class="py method">
 <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -161,7 +161,7 @@ XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Bra
 <dl class="py method">
 <dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.models.html

@@ -572,7 +572,7 @@ In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016
 <dl class="py method">
 <dt id="pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#IntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -629,7 +629,7 @@ In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016
 <dl class="py method">
 <dt id="pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ifts.html#WeightedIntervalFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -885,7 +885,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 <dl class="py method">
 <dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -903,7 +903,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 <dl class="py method">
 <dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ndata</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -921,7 +921,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 <dl class="py method">
 <dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/pwfts.html#ProbabilisticWeightedFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.models.incremental.html

@@ -116,7 +116,7 @@ window of recent lags, whose size is controlled by the parameter ‘window_lengt
 <dl class="py method">
 <dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -237,7 +237,7 @@ model, among other parameters.</p>
 <dl class="py method">
 <dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.models.multivariate.html

@@ -78,7 +78,7 @@
 <dl class="py class">
 <dt id="pyFTS.models.multivariate.FLR.FLR">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.FLR.</code><code class="sig-name descname">FLR</code><a class="reference internal" href="_modules/pyFTS/models/multivariate/FLR.html#FLR"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.FLR.FLR" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Multivariate Fuzzy Logical Relationship</p>
 <dl class="py method">
 <dt id="pyFTS.models.multivariate.FLR.FLR.set_lhs">
@@ -151,7 +151,7 @@
 <dl class="py class">
 <dt id="pyFTS.models.multivariate.variable.Variable">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.multivariate.variable.</code><code class="sig-name descname">Variable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>A variable of a fuzzy time series multivariate model. Each variable contains its own
 transformations and partitioners.</p>
 <dl class="py attribute">
@@ -514,7 +514,7 @@ multivariate fuzzy set base.</p>
 <dl class="py method">
 <dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -532,7 +532,7 @@ multivariate fuzzy set base.</p>
 <dl class="py method">
 <dt id="pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">
 <code class="sig-name descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#MVFTS.forecast_ahead_interval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</p>
+<dd><p>Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -715,7 +715,7 @@ multivariate fuzzy set base.</p>
 <dl class="py method">
 <dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">
 <code class="sig-name descname">forecast_ahead_distribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/cmvfts.html#ClusteredMVFTS.forecast_ahead_distribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Probabilistic forecast n steps ahead</p>
+<dd><p>Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.models.nonstationary.html

@@ -733,7 +733,7 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.</p>
 <span id="pyfts-models-nonstationary-util-module"></span><h2>pyFTS.models.nonstationary.util module<a class="headerlink" href="#module-pyFTS.models.nonstationary.util" title="Permalink to this headline">¶</a></h2>
 <dl class="py function">
 <dt id="pyFTS.models.nonstationary.util.plot_sets">
-<code class="sig-prename descclassname">pyFTS.models.nonstationary.util.</code><code class="sig-name descname">plot_sets</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">start</span><span class="o">=</span><span class="default_value">0</span></em>, <em class="sig-param"><span class="n">end</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[5, 5]</span></em>, <em class="sig-param"><span class="n">colors</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axes</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">data</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">window_size</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">only_lines</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/util.html#plot_sets"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.util.plot_sets" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname">pyFTS.models.nonstationary.util.</code><code class="sig-name descname">plot_sets</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">start</span><span class="o">=</span><span class="default_value">0</span></em>, <em class="sig-param"><span class="n">end</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">tam</span><span class="o">=</span><span class="default_value">[5, 5]</span></em>, <em class="sig-param"><span class="n">colors</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">save</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">file</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">axes</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">data</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">window_size</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">only_lines</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">legend</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/nonstationary/util.html#plot_sets"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.nonstationary.util.plot_sets" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py function">

docs/build/html/pyFTS.models.seasonal.html

@@ -180,7 +180,7 @@
 <dl class="py class">
 <dt id="pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.SeasonalIndexer.</code><code class="sig-name descname">SeasonalIndexer</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">num_seasons</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/SeasonalIndexer.html#SeasonalIndexer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Seasonal Indexer. Responsible to find the seasonal index of a data point inside its data set</p>
 <dl class="py method">
 <dt id="pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer.get_data">
@@ -242,7 +242,7 @@
 <dl class="py method">
 <dt id="pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/cmsfts.html#ContextualMultiSeasonalFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -301,7 +301,7 @@
 <dl class="py class">
 <dt id="pyFTS.models.seasonal.common.DateTime">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.models.seasonal.common.</code><code class="sig-name descname">DateTime</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/common.html#DateTime"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/enum.html#enum.Enum" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/enum.html#enum.Enum" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></a></p>
 <p>Data and Time granularity for time granularity and seasonality identification</p>
 <dl class="py attribute">
 <dt id="pyFTS.models.seasonal.common.DateTime.day_of_month">
@@ -479,7 +479,7 @@
 <dl class="py method">
 <dt id="pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead">
 <code class="sig-name descname">forecast_ahead</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">steps</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/msfts.html#MultiSeasonalFTS.forecast_ahead"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead" title="Permalink to this definition">¶</a></dt>
-<dd><p>Point forecast n steps ahead</p>
+<dd><p>Point forecast from 1 to H steps ahead, where H is given by the steps parameter</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/pyFTS.partitioners.html

@@ -77,7 +77,7 @@
 <dl class="py class">
 <dt id="pyFTS.partitioners.partitioner.Partitioner">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.partitioners.partitioner.</code><code class="sig-name descname">Partitioner</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/partitioner.html#Partitioner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.partitioner.Partitioner" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Universe of Discourse partitioner. Split data on several fuzzy sets</p>
 <dl class="py method">
 <dt id="pyFTS.partitioners.partitioner.Partitioner.build">
@@ -419,7 +419,7 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 
 <dl class="py function">
 <dt id="pyFTS.partitioners.FCM.fuzzy_cmeans">
-<code class="sig-prename descclassname">pyFTS.partitioners.FCM.</code><code class="sig-name descname">fuzzy_cmeans</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span></em>, <em class="sig-param"><span class="n">dados</span></em>, <em class="sig-param"><span class="n">tam</span></em>, <em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">deltadist</span><span class="o">=</span><span class="default_value">0.001</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/FCM.html#fuzzy_cmeans"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.FCM.fuzzy_cmeans" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname">pyFTS.partitioners.FCM.</code><code class="sig-name descname">fuzzy_cmeans</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span></em>, <em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">size</span></em>, <em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">deltadist</span><span class="o">=</span><span class="default_value">0.001</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/FCM.html#fuzzy_cmeans"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.FCM.fuzzy_cmeans" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
@@ -457,6 +457,13 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 
 </dd></dl>
 
+<dl class="py class">
+<dt id="pyFTS.partitioners.Grid.PreFixedGridPartitioner">
+<em class="property">class </em><code class="sig-prename descclassname">pyFTS.partitioners.Grid.</code><code class="sig-name descname">PreFixedGridPartitioner</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Grid.html#PreFixedGridPartitioner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.partitioners.Grid.PreFixedGridPartitioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.partitioners.Grid.GridPartitioner" title="pyFTS.partitioners.Grid.GridPartitioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.partitioners.Grid.GridPartitioner</span></code></a></p>
+<p>Prefixed UoD with Even Length Grid Partitioner</p>
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.partitioners.Huarng">
 <span id="pyfts-partitioners-huarng-module"></span><h2>pyFTS.partitioners.Huarng module<a class="headerlink" href="#module-pyFTS.partitioners.Huarng" title="Permalink to this headline">¶</a></h2>

docs/build/html/pyFTS.probabilistic.html

@@ -73,7 +73,7 @@
 <dl class="py class">
 <dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="sig-name descname">ProbabilityDistribution</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">type</span><span class="o">=</span><span class="default_value">'KDE'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Represents a discrete or continous probability distribution
 If type is histogram, the PDF is discrete
 If type is KDE the PDF is continuous</p>
@@ -319,7 +319,7 @@ If type is KDE the PDF is continuous</p>
 <dl class="py class">
 <dt id="pyFTS.probabilistic.kde.KernelSmoothing">
 <em class="property">class </em><code class="sig-prename descclassname">pyFTS.probabilistic.kde.</code><code class="sig-name descname">KernelSmoothing</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/kde.html#KernelSmoothing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.kde.KernelSmoothing" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Kernel Density Estimation</p>
 <dl class="py attribute">
 <dt id="pyFTS.probabilistic.kde.KernelSmoothing.h">

pyFTS/common/fts.py

@@ -72,6 +72,7 @@ class FTS(object):
         """"""
         self.is_time_variant = False
         """A boolean value indicating if this model is time variant"""
+        
 
     def fuzzy(self, data):
         """
@@ -104,7 +105,8 @@ class FTS(object):
         :param data: time series with minimal length to the order of the model
 
         :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.
-        :keyword steps_ahead: The forecasting horizon, i. e., the number of steps ahead to forecast (default value: 1)
+        :keyword steps_ahead: The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.
+        :keyword step_to: The forecasting step H, i. e., tell the model to forecast to t+H for each input sample 
         :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default value: 0)
         :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)
         :keyword nodes: a list with the dispy cluster nodes addresses
@@ -141,7 +143,9 @@ class FTS(object):
 
             steps_ahead = kw.get("steps_ahead", None)
 
-            if steps_ahead == None or steps_ahead == 1:
+            step_to = kw.get("step_to", None)
+
+            if (steps_ahead == None and step_to == None) or (steps_ahead == 1 or step_to ==1):
                 if type == 'point':
                     ret = self.forecast(ndata, **kw)
                 elif type == 'interval':
@@ -150,7 +154,7 @@ class FTS(object):
                     ret = self.forecast_distribution(ndata, **kw)
                 elif type == 'multivariate':
                     ret = self.forecast_multivariate(ndata, **kw)
-            elif steps_ahead > 1:
+            elif step_to == None and steps_ahead > 1:
                 if type == 'point':
                     ret = self.forecast_ahead(ndata, steps_ahead, **kw)
                 elif type == 'interval':
@@ -159,6 +163,11 @@ class FTS(object):
                     ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kw)
                 elif type == 'multivariate':
                     ret = self.forecast_ahead_multivariate(ndata, steps_ahead, **kw)
+            elif step_to > 1:
+                if type == 'point':
+                    ret = self.forecast_step(ndata, step_to, **kw)
+                else:
+                    raise NotImplementedError('This model only perform point step ahead forecasts!')
 
             if not ['point', 'interval', 'distribution', 'multivariate'].__contains__(type):
                 raise ValueError('The argument \'type\' has an unknown value.')
@@ -227,9 +236,10 @@ class FTS(object):
         """
         raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
 
+
     def forecast_ahead(self, data, steps, **kwargs):
         """
-        Point forecast n steps ahead
+        Point forecast from 1 to H steps ahead, where H is given by the steps parameter
 
         :param data: time series data with the minimal length equal to the max_lag of the model
         :param steps: the number of steps ahead to forecast (default: 1)
@@ -259,7 +269,7 @@ class FTS(object):
 
     def forecast_ahead_interval(self, data, steps, **kwargs):
         """
-        Interval forecast n steps ahead
+        Interval forecast from 1 to H steps ahead, where H is given by the steps parameter
 
         :param data: time series data with the minimal length equal to the max_lag of the model
         :param steps: the number of steps ahead to forecast
@@ -270,7 +280,7 @@ class FTS(object):
 
     def forecast_ahead_distribution(self, data, steps, **kwargs):
         """
-        Probabilistic forecast n steps ahead
+        Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter
 
         :param data: time series data with the minimal length equal to the max_lag of the model
         :param steps: the number of steps ahead to forecast
@@ -290,6 +300,39 @@ class FTS(object):
         """
         raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
 
+    def forecast_step(self, data, step, **kwargs):
+        """
+        Point forecast for H steps ahead, where H is given by the step parameter
+
+        :param data: time series data with the minimal length equal to the max_lag of the model
+        :param step: the forecasting horizon (default: 1)
+        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)
+        :return: a list with the forecasted values
+        """
+
+        l = len(data)
+
+        ret = []
+
+        if l < self.max_lag:
+            return data
+
+        if isinstance(data, np.ndarray):
+            data = data.tolist()
+
+        start = kwargs.get('start_at',0)
+
+        for k in np.arange(start+self.max_lag, l):
+            sample = data[k-self.max_lag:k]
+            tmp = self.forecast_ahead(sample, step, **kwargs)
+
+            if isinstance(tmp,(list, np.ndarray)):
+                tmp = tmp[-1]
+
+            ret.append(tmp)
+
+        return ret
+
     def train(self, data, **kwargs):
         """
         Method specific parameter fitting

pyFTS/tests/general.py

@@ -14,7 +14,7 @@ from pyFTS.benchmarks import benchmarks as bchmk, Measures
 from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
 from pyFTS.models.ensemble import ensemble
 from pyFTS.common import Transformations, Membership, Util
-from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn
+from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn
 from pyFTS.fcm import fts, common, GA
 from pyFTS.common import Transformations
 
@@ -22,32 +22,26 @@ tdiff = Transformations.Differential(1)
 
 boxcox = Transformations.BoxCox(0)
 
-df = pd.read_csv('https://query.data.world/s/l3u4gqbrbm5ymo6ghxl7jmxed7sgyk')
-dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
-dados = dados.flatten().tolist()
+df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
+#dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
+dados = df['temperature'].values
+#dados = dados.flatten().tolist()
 
-qtde_dt_tr = 150
-dados_treino = dados[:qtde_dt_tr]
+l = len(dados)
 
-#print(dados_treino)
+dados_treino = dados[:int(l*.7)]
+dados_teste = dados[int(l*.7):]
 
-ttr = list(range(len(dados_treino)))
+particionador = Grid.GridPartitioner(data = dados_treino, npart = 15, func = Membership.trimf)
 
-ordem = 1 # ordem do modelo, indica quantos ultimos valores serao usados
-
-dados_teste = dados[qtde_dt_tr - ordem:250]
-tts = list(range(len(dados_treino) - ordem, len(dados_treino) + len(dados_teste) - ordem))
-
-particionador = Grid.GridPartitioner(data = dados_treino, npart = 30, func = Membership.trimf)
-
-modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = ordem)
+modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 2)
 
 modelo.fit(dados_treino)
 
-print(modelo)
+# print(modelo)
 
 # Todo o procedimento de inferência é feito pelo método predict
-predicoes = modelo.predict(dados_teste[38:40])
+predicoes = modelo.predict(dados_teste, step_to=30)
 
 print(predicoes)