# pyFTS.models.seasonal package

## Submodules

## pyFTS.models.seasonal.SeasonalIndexer module

class `pyFTS.models.seasonal.SeasonalIndexer.``DataFrameSeasonalIndexer`(index_fields, index_seasons, data_field, **kwargs)

  Bases: `pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer`

  Use the Pandas.DataFrame index position to index the seasonality

  `get_data`(data)
  `get_data_by_season`(data, indexes)
  `get_index_by_season`(indexes)
  `get_season_by_index`(index)
  `get_season_of_data`(data)
  `set_data`(data, value)

class `pyFTS.models.seasonal.SeasonalIndexer.``DateTimeSeasonalIndexer`(date_field, index_fields, index_seasons, data_field, **kwargs)

  Bases: `pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer`

  Use a Pandas.DataFrame date field to index the seasonality

  `get_data`(data)
  `get_data_by_season`(data, indexes)
  `get_index`(data)
  `get_index_by_season`(indexes)
  `get_season_by_index`(index)
  `get_season_of_data`(data)
  `set_data`(data, value)

class `pyFTS.models.seasonal.SeasonalIndexer.``LinearSeasonalIndexer`(seasons, units, ignore=None, **kwargs)

  Bases: `pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer`

  Use the data array/list position to index the seasonality

  `get_data`(data)
  `get_index_by_season`(indexes)
  `get_season_by_index`(index)
  `get_season_of_data`(data)

class `pyFTS.models.seasonal.SeasonalIndexer.``SeasonalIndexer`(num_seasons, **kwargs)

  Bases: `object`

  Seasonal Indexer. Responsible to find the seasonal index of a data point inside its data set

  `get_data`(data)
  `get_data_by_season`(data, indexes)
  `get_index`(data)
  `get_index_by_season`(indexes)
  `get_season_by_index`(inde)
  `get_season_of_data`(data)

## pyFTS.models.seasonal.cmsfts module

class `pyFTS.models.seasonal.cmsfts.``ContextualMultiSeasonalFTS`(**kwargs)

  Bases: `pyFTS.models.seasonal.sfts.SeasonalFTS`

  Contextual Multi-Seasonal Fuzzy Time Series

  `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

  `generate_flrg`(flrs)
  `get_midpoints`(flrg, data)
  `train`(data, **kwargs)

    Method specific parameter fitting

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

class `pyFTS.models.seasonal.cmsfts.``ContextualSeasonalFLRG`(seasonality)

  Bases: `pyFTS.models.seasonal.sfts.SeasonalFLRG`

  Contextual Seasonal Fuzzy Logical Relationship Group

  `append_rhs`(flr, **kwargs)

## pyFTS.models.seasonal.common module

class `pyFTS.models.seasonal.common.``DateTime`

  Bases: `enum.Enum`

  An enumeration.

  `day_of_month` = 30
  `day_of_week` = 7
  `day_of_year` = 364
  `hour` = 6
  `hour_of_day` = 24
  `hour_of_month` = 744
  `hour_of_week` = 168
  `hour_of_year` = 8736
  `minute_of_day` = 1440
  `minute_of_hour` = 60
  `minute_of_month` = 44640
  `minute_of_week` = 10080
  `minute_of_year` = 524160
  `month` = 12
  `second` = 8
  `second_of_day` = 86400
  `second_of_hour` = 3600
  `second_of_minute` = 60.00001
  `year` = 1

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

  Bases: `pyFTS.common.FuzzySet.FuzzySet`

  Temporal/Seasonal 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.models.seasonal.common.``strip_datepart`(date, date_part)

## pyFTS.models.seasonal.msfts module

class `pyFTS.models.seasonal.msfts.``MultiSeasonalFTS`(name, indexer, **kwargs)

  Bases: `pyFTS.models.seasonal.sfts.SeasonalFTS`

  Multi-Seasonal Fuzzy Time Series

  `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

  `generate_flrg`(flrs)
  `train`(data, **kwargs)

    Method specific parameter fitting

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

## pyFTS.models.seasonal.partitioner module

class `pyFTS.models.seasonal.partitioner.``TimeGridPartitioner`(**kwargs)

  Bases: `pyFTS.partitioners.partitioner.Partitioner`

  Even Length DateTime Grid Partitioner

  `build`(data)

    Perform the partitioning of the Universe of Discourse

    Parameters: | data – training data
    Returns: |

  `plot`(ax)

    Plot the :param ax: :return:

## pyFTS.models.seasonal.sfts module

Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)

17. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.

S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.

class `pyFTS.models.seasonal.sfts.``SeasonalFLRG`(seasonality)

  Bases: `pyFTS.common.flrg.FLRG`

  First Order Seasonal Fuzzy Logical Relationship Group

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

    Returns a unique identifier for this FLRG

class `pyFTS.models.seasonal.sfts.``SeasonalFTS`(**kwargs)

  Bases: `pyFTS.common.fts.FTS`

  First Order Seasonal Fuzzy Time Series

  `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

  `generate_flrg`(flrs)
  `get_midpoints`(flrg)
  `train`(data, **kwargs)

    Method specific parameter fitting

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

## Module contents