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