pyFTS.models.seasonal package

Submodules

pyFTS.models.seasonal.SeasonalIndexer module

class pyFTS.models.seasonal.SeasonalIndexer.DataFrameSeasonalIndexer(index_fields, index_seasons, data_field, **kwargs)[source]

Bases: pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer

Use the Pandas.DataFrame index position to index the seasonality

get_data(data)[source]
get_data_by_season(data, indexes)[source]
get_index_by_season(indexes)[source]
get_season_by_index(index)[source]
get_season_of_data(data)[source]
set_data(data, value)[source]
class pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer(date_field, index_fields, index_seasons, data_field, **kwargs)[source]

Bases: pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer

Use a Pandas.DataFrame date field to index the seasonality

get_data(data)[source]
get_data_by_season(data, indexes)[source]
get_index(data)[source]
get_index_by_season(indexes)[source]
get_season_by_index(index)[source]
get_season_of_data(data)[source]
set_data(data, value)[source]
class pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer(seasons, units, ignore=None, **kwargs)[source]

Bases: pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer

Use the data array/list position to index the seasonality

get_data(data)[source]
get_index_by_season(indexes)[source]
get_season_by_index(index)[source]
get_season_of_data(data)[source]
class pyFTS.models.seasonal.SeasonalIndexer.SeasonalIndexer(num_seasons, **kwargs)[source]

Bases: object

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

get_data(data)[source]
get_data_by_season(data, indexes)[source]
get_index(data)[source]
get_index_by_season(indexes)[source]
get_season_by_index(inde)[source]
get_season_of_data(data)[source]

pyFTS.models.seasonal.cmsfts module

pyFTS.models.seasonal.common module

class pyFTS.models.seasonal.common.DateTime[source]

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 = 7
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)[source]

Bases: pyFTS.common.FuzzySet.FuzzySet

Temporal/Seasonal Fuzzy Set

membership(x)[source]

Calculate the membership value of a given input

Parameters:x – input value
Returns:membership value of x at this fuzzy set
pyFTS.models.seasonal.common.strip_datepart(date, date_part)[source]

pyFTS.models.seasonal.msfts module

pyFTS.models.seasonal.partitioner module

class pyFTS.models.seasonal.partitioner.TimeGridPartitioner(**kwargs)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

Even Length DateTime Grid Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:
plot(ax)[source]

Plot the :param ax: :return:

pyFTS.models.seasonal.sfts module

Module contents