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

pyFTS.models.seasonal.common module

class pyFTS.models.seasonal.common.DateTime(value)

Bases: enum.Enum

Data and Time granularity for time granularity and seasonality identification

day_of_month = 30

day_of_week = 7

day_of_year = 364

half = 2

hour = 24

hour_of_day = 24

hour_of_month = 744

hour_of_week = 168

hour_of_year = 8736

minute = 60

minute_of_day = 1440

minute_of_hour = 60

minute_of_month = 44640

minute_of_week = 10080

minute_of_year = 524160

month = 12

quarter = 4

second = 60

second_of_day = 86400

second_of_hour = 3600

second_of_minute = 60.00001

sixth = 6

third = 3

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, mask='')

pyFTS.models.seasonal.msfts module

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

build_index()

extractor(x)

Extract a single primitive type from an structured instance

mask

A string with datetime formating mask

plot(ax)

Plot the :param ax: :return:

search(data, **kwargs)

Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several overlapped fuzzy sets.

Parameters

• data – the value to search for the nearest fuzzy sets

• type – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.

• results – the number of nearest fuzzy sets to return

Returns

a list with the nearest fuzzy sets

season

Seasonality, a pyFTS.models.seasonal.common.DateTime object

pyFTS.models.seasonal.sfts module

Module contents