# pyFTS.common package

## Module contents

## Submodules

## pyFTS.common.Composite module

Composite Fuzzy Sets

class `pyFTS.common.Composite.``FuzzySet`(name, superset=False, **kwargs)

  Bases: `pyFTS.common.FuzzySet.FuzzySet`

  Composite Fuzzy Set

  `append`(mf, parameters)

    Adds a new function to composition

    Parameters: | mf – parameters –
    Returns: |

  `append_set`(set)

    Adds a new function to composition

    Parameters: | mf – parameters –
    Returns: |

  `membership`(x)

    Calculate the membership value of a given input

    Parameters: | x – input value
    Returns: | membership value of x at this fuzzy set

  `transform`(x)

    Preprocess the data point for non native types

    Parameters: | x –
    Returns: | return a native type value for the structured type

## pyFTS.common.FLR module

This module implements functions for Fuzzy Logical Relationship generation

class `pyFTS.common.FLR.``FLR`(LHS, RHS)

  Bases: `object`

  Fuzzy Logical Relationship

  Represents a temporal transition of the fuzzy set LHS on time t for the fuzzy set RHS on time t+1.

  `LHS` = None

    Left Hand Side fuzzy set

  `RHS` = None

    Right Hand Side fuzzy set

class `pyFTS.common.FLR.``IndexedFLR`(index, LHS, RHS)

  Bases: `pyFTS.common.FLR.FLR`

  Season Indexed Fuzzy Logical Relationship

  `index` = None

    seasonal index

`pyFTS.common.FLR.``generate_high_order_recurrent_flr`(fuzzyData)

  Create a ordered FLR set from a list of fuzzy sets with recurrence

  Parameters: | fuzzyData – ordered list of fuzzy sets
  Returns: | ordered list of FLR

`pyFTS.common.FLR.``generate_indexed_flrs`(sets, indexer, data, transformation=None, alpha_cut=0.0)

  Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence

  Parameters: | sets – fuzzy sets indexer – seasonality indexer data – original data
  Returns: | ordered list of FLR

`pyFTS.common.FLR.``generate_non_recurrent_flrs`(fuzzyData)

  Create a ordered FLR set from a list of fuzzy sets without recurrence

  Parameters: | fuzzyData – ordered list of fuzzy sets
  Returns: | ordered list of FLR

`pyFTS.common.FLR.``generate_recurrent_flrs`(fuzzyData)

  Create a ordered FLR set from a list of fuzzy sets with recurrence

  Parameters: | fuzzyData – ordered list of fuzzy sets
  Returns: | ordered list of FLR

## pyFTS.common.FuzzySet module

class `pyFTS.common.FuzzySet.``FuzzySet`(name, mf, parameters, centroid, alpha=1.0, **kwargs)

  Bases: `object`

  Fuzzy Set

  `Z` = None

    Partition function in respect to the membership function

  `alpha` = None

    The alpha cut value

  `centroid` = None

    The fuzzy set center of mass (or midpoint)

  `membership`(x)

    Calculate the membership value of a given input

    Parameters: | x – input value
    Returns: | membership value of x at this fuzzy set

  `mf` = None

    The membership function

  `name` = None

    The fuzzy set name

  `parameters` = None

    The parameters of the membership function

  `partition_function`(uod=None, nbins=100)

    Calculate the partition function over the membership function.

    Parameters: | uod – nbins –
    Returns: |

  `transform`(x)

    Preprocess the data point for non native types

    Parameters: | x –
    Returns: | return a native type value for the structured type

  `type` = None

    The fuzzy set type (common, composite, nonstationary, etc)

  `variable` = None

    In multivariate time series, indicate for which variable this fuzzy set belogs

`pyFTS.common.FuzzySet.``check_bounds`(data, fuzzy_sets, ordered_sets)

`pyFTS.common.FuzzySet.``check_bounds_index`(data, fuzzy_sets, ordered_sets)

`pyFTS.common.FuzzySet.``fuzzyfy`(data, partitioner, **kwargs)

  A general method for fuzzyfication.

  Parameters: | data – input value to be fuzzyfied partitioner – a trained pyFTS.partitioners.Partitioner object kwargs – dict, optional arguments alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’) method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership) mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership

  values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) ) :returns a list with the fuzzyfied values, depending on the mode

`pyFTS.common.FuzzySet.``fuzzyfy_instance`(inst, fuzzy_sets, ordered_sets=None)

  Calculate the membership values for a data point given fuzzy sets

  Parameters: | inst – data point fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object. ordered_sets – a list with the fuzzy sets names ordered by their centroids.
  Returns: | array of membership values

`pyFTS.common.FuzzySet.``fuzzyfy_instances`(data, fuzzy_sets, ordered_sets=None)

  Calculate the membership values for a data point given fuzzy sets

  Parameters: | inst – data point fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object. ordered_sets – a list with the fuzzy sets names ordered by their centroids.
  Returns: | array of membership values

`pyFTS.common.FuzzySet.``fuzzyfy_series`(data, fuzzy_sets, method='maximum', alpha_cut=0.0, ordered_sets=None)

`pyFTS.common.FuzzySet.``fuzzyfy_series_old`(data, fuzzy_sets, method='maximum')

`pyFTS.common.FuzzySet.``get_fuzzysets`(inst, fuzzy_sets, ordered_sets=None, alpha_cut=0.0)

  Return the fuzzy sets which membership value for a inst is greater than the alpha_cut

  Parameters: | inst – data point fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object. ordered_sets – a list with the fuzzy sets names ordered by their centroids. alpha_cut – Minimal membership to be considered on fuzzyfication process
  Returns: | array of membership values

`pyFTS.common.FuzzySet.``get_maximum_membership_fuzzyset`(inst, fuzzy_sets, ordered_sets=None)

  Fuzzify a data point, returning the fuzzy set with maximum membership value

  Parameters: | inst – data point fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object. ordered_sets – a list with the fuzzy sets names ordered by their centroids.
  Returns: | fuzzy set with maximum membership

`pyFTS.common.FuzzySet.``get_maximum_membership_fuzzyset_index`(inst, fuzzy_sets)

  Fuzzify a data point, returning the fuzzy set with maximum membership value

  Parameters: | inst – data point fuzzy_sets – dict of fuzzy sets
  Returns: | fuzzy set with maximum membership

`pyFTS.common.FuzzySet.``grant_bounds`(data, fuzzy_sets, ordered_sets)

`pyFTS.common.FuzzySet.``set_ordered`(fuzzy_sets)

  Order a fuzzy set list by their centroids

  Parameters: | fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
  Returns: | a list with the fuzzy sets names ordered by their centroids.

## pyFTS.common.Membership module

Membership functions for Fuzzy Sets

`pyFTS.common.Membership.``bellmf`(x, parameters)

  Bell shaped membership function

  Parameters: | x – parameters –
  Returns: |

`pyFTS.common.Membership.``gaussmf`(x, parameters)

  Gaussian fuzzy membership function

  Parameters: | x – data point parameters – a list with 2 real values (mean and variance)
  Returns: | the membership value of x given the parameters

`pyFTS.common.Membership.``sigmf`(x, parameters)

  Sigmoid / Logistic membership function

  Parameters: | x – parameters – an list with 2 real values (smoothness and midpoint)

  :return

`pyFTS.common.Membership.``singleton`(x, parameters)

  Singleton membership function, a single value fuzzy function

  Parameters: | x – parameters – a list with one real value

  :returns

`pyFTS.common.Membership.``trapmf`(x, parameters)

  Trapezoidal fuzzy membership function

  Parameters: | x – data point parameters – a list with 4 real values
  Returns: | the membership value of x given the parameters

`pyFTS.common.Membership.``trimf`(x, parameters)

  Triangular fuzzy membership function

  Parameters: | x – data point parameters – a list with 3 real values
  Returns: | the membership value of x given the parameters

## pyFTS.common.SortedCollection module

class `pyFTS.common.SortedCollection.``SortedCollection`(iterable=(), key=None)

  Bases: `object`

  Sequence sorted by a key function.

  SortedCollection() is much easier to work with than using bisect() directly. It supports key functions like those use in sorted(), min(), and max(). The result of the key function call is saved so that keys can be searched efficiently.

  Instead of returning an insertion-point which can be hard to interpret, the five find-methods return a specific item in the sequence. They can scan for exact matches, the last item less-than-or-equal to a key, or the first item greater-than-or-equal to a key.

  Once found, an item’s ordinal position can be located with the index() method. New items can be added with the insert() and insert_right() methods. Old items can be deleted with the remove() method.

  The usual sequence methods are provided to support indexing, slicing, length lookup, clearing, copying, forward and reverse iteration, contains checking, item counts, item removal, and a nice looking repr.

  Finding and indexing are O(log n) operations while iteration and insertion are O(n). The initial sort is O(n log n).

  The key function is stored in the ‘key’ attibute for easy introspection or so that you can assign a new key function (triggering an automatic re-sort).

  In short, the class was designed to handle all of the common use cases for bisect but with a simpler API and support for key functions.

      >>> from pprint import pprint
      >>> from operator import itemgetter

      >>> s = SortedCollection(key=itemgetter(2))
      >>> for record in [
      ...         ('roger', 'young', 30),
      ...         ('angela', 'jones', 28),
      ...         ('bill', 'smith', 22),
      ...         ('david', 'thomas', 32)]:
      ...     s.insert(record)

      >>> pprint(list(s))         # show records sorted by age
      [('bill', 'smith', 22),
       ('angela', 'jones', 28),
       ('roger', 'young', 30),
       ('david', 'thomas', 32)]

      >>> s.find_le(29)           # find oldest person aged 29 or younger
      ('angela', 'jones', 28)
      >>> s.find_lt(28)           # find oldest person under 28
      ('bill', 'smith', 22)
      >>> s.find_gt(28)           # find youngest person over 28
      ('roger', 'young', 30)

      >>> r = s.find_ge(32)       # find youngest person aged 32 or older
      >>> s.index(r)              # get the index of their record
      3
      >>> s[3]                    # fetch the record at that index
      ('david', 'thomas', 32)

      >>> s.key = itemgetter(0)   # now sort by first name
      >>> pprint(list(s))
      [('angela', 'jones', 28),
       ('bill', 'smith', 22),
       ('david', 'thomas', 32),
       ('roger', 'young', 30)]

  `around`(k)
  `between`(ge, le)
  `clear`()
  `copy`()
  `count`(item)

    Return number of occurrences of item

  `find`(k)

    Return first item with a key == k. Raise ValueError if not found.

  `find_ge`(k)

    Return first item with a key >= equal to k. Raise ValueError if not found

  `find_gt`(k)

    Return first item with a key > k. Raise ValueError if not found

  `find_le`(k)

    Return last item with a key <= k. Raise ValueError if not found.

  `find_lt`(k)

    Return last item with a key < k. Raise ValueError if not found.

  `index`(item)

    Find the position of an item. Raise ValueError if not found.

  `insert`(item)

    Insert a new item. If equal keys are found, add to the left

  `insert_right`(item)

    Insert a new item. If equal keys are found, add to the right

  `inside`(ge, le)
  `key`

    key function

  `remove`(item)

    Remove first occurence of item. Raise ValueError if not found

## pyFTS.common.Transformations module

Common data transformation used on pre and post processing of the FTS

class `pyFTS.common.Transformations.``AdaptiveExpectation`(parameters)

  Bases: `pyFTS.common.Transformations.Transformation`

  Adaptive Expectation post processing

  `apply`(data, param=None, **kwargs)

    Apply the transformation on input data

    Parameters: | data – input data param – kwargs –
    Returns: | numpy array with transformed data

  `inverse`(data, param, **kwargs)

    Parameters: | data – transformed data param – kwargs –
    Returns: | numpy array with inverse transformed data

  `parameters`

class `pyFTS.common.Transformations.``BoxCox`(plambda)

  Bases: `pyFTS.common.Transformations.Transformation`

  Box-Cox power transformation

  `apply`(data, param=None, **kwargs)

    Apply the transformation on input data

    Parameters: | data – input data param – kwargs –
    Returns: | numpy array with transformed data

  `inverse`(data, param=None, **kwargs)

    Parameters: | data – transformed data param – kwargs –
    Returns: | numpy array with inverse transformed data

  `parameters`

class `pyFTS.common.Transformations.``Differential`(lag)

  Bases: `pyFTS.common.Transformations.Transformation`

  Differentiation data transform

  `apply`(data, param=None, **kwargs)

    Apply the transformation on input data

    Parameters: | data – input data param – kwargs –
    Returns: | numpy array with transformed data

  `inverse`(data, param, **kwargs)

    Parameters: | data – transformed data param – kwargs –
    Returns: | numpy array with inverse transformed data

  `parameters`

class `pyFTS.common.Transformations.``Scale`(min=0, max=1)

  Bases: `pyFTS.common.Transformations.Transformation`

  Scale data inside a interval [min, max]

  `apply`(data, param=None, **kwargs)

    Apply the transformation on input data

    Parameters: | data – input data param – kwargs –
    Returns: | numpy array with transformed data

  `inverse`(data, param, **kwargs)

    Parameters: | data – transformed data param – kwargs –
    Returns: | numpy array with inverse transformed data

  `parameters`

class `pyFTS.common.Transformations.``Transformation`(**kwargs)

  Bases: `object`

  Data transformation used on pre and post processing of the FTS

  `apply`(data, param, **kwargs)

    Apply the transformation on input data

    Parameters: | data – input data param – kwargs –
    Returns: | numpy array with transformed data

  `inverse`(data, param, **kwargs)

    Parameters: | data – transformed data param – kwargs –
    Returns: | numpy array with inverse transformed data

`pyFTS.common.Transformations.``Z`(original)

`pyFTS.common.Transformations.``aggregate`(original, operation)

`pyFTS.common.Transformations.``roi`(original)

`pyFTS.common.Transformations.``smoothing`(original, lags)

## pyFTS.common.Util module

Common facilities for pyFTS

`pyFTS.common.Util.``current_milli_time`()

`pyFTS.common.Util.``draw_sets_on_axis`(axis, model, size)

`pyFTS.common.Util.``enumerate2`(xs, start=0, step=1)

`pyFTS.common.Util.``load_env`(file)

`pyFTS.common.Util.``load_obj`(file)

  Load to memory an object stored filesystem. This function depends on Dill package

  Parameters: | file – file name where the object is stored
  Returns: | object

`pyFTS.common.Util.``persist_env`(file)

  Persist an entire environment on file. This function depends on Dill package

  Parameters: | file – file name to store the environment

`pyFTS.common.Util.``persist_obj`(obj, file)

  Persist an object on filesystem. This function depends on Dill package

  Parameters: | obj – object on memory file – file name to store the object

`pyFTS.common.Util.``plot_compared_intervals_ahead`(original, models, colors, distributions, time_from, time_to, intervals=True, save=False, file=None, tam=[20, 5], resolution=None, cmap='Blues', linewidth=1.5)

  Plot the forecasts of several one step ahead models, by point or by interval

  Parameters: | original – Original time series data (list) models – List of models to compare colors – List of models colors distributions – True to plot a distribution time_from – index of data poit to start the ahead forecasting time_to – number of steps ahead to forecast interpol – Fill space between distribution plots save – Save the picture on file file – Filename to save the picture tam – Size of the picture resolution – cmap – Color map to be used on distribution plot option – Distribution type to be passed for models
  Returns: |

`pyFTS.common.Util.``plot_density_rectange`(ax, cmap, density, fig, resolution, time_from, time_to)

  Auxiliar function to plot_compared_intervals_ahead

`pyFTS.common.Util.``plot_distribution`(ax, cmap, probabilitydist, fig, time_from, reference_data=None)

  Plot forecasted ProbabilityDistribution objects on a matplotlib axis

  Parameters: | ax – matplotlib axis cmap – matplotlib colormap name probabilitydist – list of ProbabilityDistribution objects fig – matplotlib figure time_from – starting time (on x axis) to begin the plots reference_data –
  Returns: |

`pyFTS.common.Util.``plot_interval`(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1)

  Plot forecasted intervals on matplotlib

  Parameters: | axis – matplotlib axis intervals – list of forecasted intervals order – order of the model that create the forecasts label – figure label color – matplotlib color name typeonlegend – ls – matplotlib line style linewidth – matplotlib width
  Returns: |

`pyFTS.common.Util.``plot_probability_distributions`(pmfs, lcolors, tam=[15, 7])

`pyFTS.common.Util.``plot_rules`(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1)

  Plot the FLRG rules of a FTS model on a matplotlib axis

  Parameters: | model – FTS model size – figure size axis – matplotlib axis rules_by_axis – number of rules plotted by column columns – number of columns
  Returns: |

`pyFTS.common.Util.``show_and_save_image`(fig, file, flag, lgd=None)

  Show and image and save on file

  Parameters: | fig – Matplotlib Figure object file – filename to save the picture flag – if True the image will be saved lgd – legend

`pyFTS.common.Util.``sliding_window`(data, windowsize, train=0.8, inc=0.1, **kwargs)

  Sliding window method of cross validation for time series

  Parameters: | data – the entire dataset windowsize – window size train – percentual of the window size will be used for training the models inc – percentual of data used for slide the window
  Returns: | window count, training set, test set

`pyFTS.common.Util.``uniquefilename`(name)

## pyFTS.common.flrg module

class `pyFTS.common.flrg.``FLRG`(order, **kwargs)

  Bases: `object`

  Fuzzy Logical Relationship Group

  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.

  `LHS` = None

    Left Hand Side of the rule

  `RHS` = None

    Right Hand Side of the rule

  `append_rhs`(set, **kwargs)
  `get_key`()

    Returns a unique identifier for this FLRG

  `get_lower`(sets)

    Returns the lower bound value for the RHS fuzzy sets

    Parameters: | sets – fuzzy sets
    Returns: | lower bound value

  `get_membership`(data, sets)

    Returns the membership value of the FLRG for the input data

    Parameters: | data – input data sets – fuzzy sets
    Returns: | the membership value

  `get_midpoint`(sets)

    Returns the midpoint value for the RHS fuzzy sets

    Parameters: | sets – fuzzy sets
    Returns: | the midpoint value

  `get_midpoints`(sets)
  `get_upper`(sets)

    Returns the upper bound value for the RHS fuzzy sets

    Parameters: | sets – fuzzy sets
    Returns: | upper bound value

  `order` = None

    Number of lags on LHS

  `reset_calculated_values`()

## pyFTS.common.fts module

class `pyFTS.common.fts.``FTS`(**kwargs)

  Bases: `object`

  Fuzzy Time Series object model

  `alpha_cut` = None

    A float with the minimal membership to be considered on fuzzyfication process

  `append_log`(operation, value)
  `append_rule`(flrg)

    Append FLRG rule to the model

    Parameters: | flrg – rule
    Returns: |

  `append_transformation`(transformation)
  `apply_inverse_transformations`(data, params=None, **kwargs)

    Apply the data transformations for data postprocessing

    Parameters: | data – input data params – transformation parameters updateUoD – kwargs –
    Returns: | postprocessed data

  `apply_transformations`(data, params=None, updateUoD=False, **kwargs)

    Apply the data transformations for data preprocessing

    Parameters: | data – input data params – transformation parameters updateUoD – kwargs –
    Returns: | preprocessed data

  `auto_update` = None

    A boolean value indicating that model is incremental

  `benchmark_only` = None

    A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles.

  `clone_parameters`(model)

    Import the parameters values from other model

    Parameters: | model –

  `detail` = None

    A string with the model detailed information

  `fit`(ndata, **kwargs)

    Fit the model’s parameters based on the training data.

    Parameters: | ndata – training time series data kwargs – num_batches – split the training data in num_batches to save memory during the training process save_model – save final model on disk batch_save – save the model between each batch file_path – path to save the model distributed – boolean, indicate if the training procedure will be distributed in a dispy cluster nodes – a list with the dispy cluster nodes addresses

  `flrgs` = None

    The list of Fuzzy Logical Relationship Groups - FLRG

  `forecast`(data, **kwargs)

    Point forecast one step ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model kwargs – model specific parameters
    Returns: | a list with the forecasted values

  `forecast_ahead`(data, steps, **kwargs)

    Point forecast n steps ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model steps – the number of steps ahead to forecast start – in the multi step forecasting, the index of the data where to start forecasting
    Returns: | a list with the forecasted values

  `forecast_ahead_distribution`(data, steps, **kwargs)

    Probabilistic forecast n steps ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model steps – the number of steps ahead to forecast kwargs – model specific parameters
    Returns: | a list with the forecasted Probability Distributions

  `forecast_ahead_interval`(data, steps, **kwargs)

    Interval forecast n steps ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model steps – the number of steps ahead to forecast kwargs – model specific parameters
    Returns: | a list with the forecasted intervals

  `forecast_ahead_multivariate`(data, steps, **kwargs)

    Multivariate forecast n step ahead

    Parameters: | data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model steps – the number of steps ahead to forecast kwargs – model specific parameters
    Returns: | a Pandas Dataframe object representing the forecasted values for each variable

  `forecast_distribution`(data, **kwargs)

    Probabilistic forecast one step ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model kwargs – model specific parameters
    Returns: | a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

  `forecast_interval`(data, **kwargs)

    Interval forecast one step ahead

    Parameters: | data – time series data with the minimal length equal to the max_lag of the model kwargs – model specific parameters
    Returns: | a list with the prediction intervals

  `forecast_multivariate`(data, **kwargs)

    Multivariate forecast one step ahead

    Parameters: | data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model kwargs – model specific parameters
    Returns: | a Pandas Dataframe object representing the forecasted values for each variable

  `fuzzy`(data)

    Fuzzify a data point

    Parameters: | data – data point
    Returns: | maximum membership fuzzy set

  `get_UoD`()
  `has_interval_forecasting` = None

    A boolean value indicating if the model supports interval forecasting, default: False

  `has_point_forecasting` = None

    A boolean value indicating if the model supports point forecasting, default: True

  `has_probability_forecasting` = None

    A boolean value indicating if the model support probabilistic forecasting, default: False

  `has_seasonality` = None

    A boolean value indicating if the model supports seasonal indexers, default: False

  `indexer` = None

    An pyFTS.models.seasonal.Indexer object for indexing the time series data

  `is_clustered` = None

    A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like a monovariate method, default: False

  `is_high_order` = None

    A boolean value indicating if the model support orders greater than 1, default: False

  `is_multivariate` = None

    A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False

  `is_wrapper` = None

    Indicates that this model is a wrapper for other(s) method(s)

  `lags` = None

    The list of lag indexes for high order models

  `len_total`()

    Total length of the model, adding the number of terms in all rules

    Returns: |

  `log` = None
  `max_lag` = None

    A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags needed to forecast a single step ahead

  `merge`(model)

    Merge the FLRG rules from other model

    Parameters: | model – source model
    Returns: |

  `min_order` = None

    In high order models, this integer value indicates the minimal order supported for the model, default: 1

  `name` = None

    A string with the model name

  `order` = None

    A integer with the model order (number of past lags are used on forecasting)

  `original_max` = None

    A float with the upper limit of the Universe of Discourse, the maximal value found on training data

  `original_min` = None

    A float with the lower limit of the Universe of Discourse, the minimal value found on training data

  `partitioner` = None

    A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy.

  `predict`(data, **kwargs)

    Forecast using trained model

    Parameters: | data – time series with minimal length to the order of the model type – the forecasting type, one of these values: point(default), interval, distribution or multivariate. steps_ahead – The forecasting horizon, i. e., the number of steps ahead to forecast start – in the multi step forecasting, the index of the data where to start forecasting distributed – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster nodes – a list with the dispy cluster nodes addresses explain – try to explain, step by step, the one-step-ahead point forecasting result given the input data. generators – for multivariate methods on multi step ahead forecasting, generators is a dict where the keys are the dataframe columun names (except the target_variable) and the values are lambda functions that accept one value (the actual value of the variable) and return the next value or trained FTS models that accept the actual values and forecast new ones.
    Returns: | a numpy array with the forecasted data

  `reset_calculated_values`()

    Reset all pre-calculated values on the FLRG’s

    Returns: |

  `sets` = None

    The list of fuzzy sets used on this model

  `shortname` = None

    A string with a short name or alias for the model

  `train`(data, **kwargs)

    Method specific parameter fitting

    Parameters: | data – training time series data kwargs – Method specific parameters

  `transformations` = None

    A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []

  `transformations_param` = None

    A list with the specific parameters for each data transformation

  `uod_clip` = None

    Flag indicating if the test data will be clipped inside the training Universe of Discourse

## pyFTS.common.tree module

Tree data structure

class `pyFTS.common.tree.``FLRGTree`

  Bases: `object`

  Represents a FLRG set with a tree structure

class `pyFTS.common.tree.``FLRGTreeNode`(value)

  Bases: `object`

  Tree node for

  `appendChild`(child)
  `getChildren`()
  `getStr`(k)
  `paths`(acc=[])

`pyFTS.common.tree.``build_tree_without_order`(node, lags, level)

`pyFTS.common.tree.``flat`(dados)