pyFTS.models.multivariate package¶

Module contents¶

Multivariate Fuzzy Time Series methods

Submodules¶

pyFTS.models.multivariate.FLR module¶

class pyFTS.models.multivariate.FLR.FLR[source]¶

Bases: object

Multivariate Fuzzy Logical Relationship

set_lhs(var, set)[source]¶

set_rhs(set)[source]¶

pyFTS.models.multivariate.common module¶

pyFTS.models.multivariate.common.fuzzyfy_instance(data_point, var)[source]¶

pyFTS.models.multivariate.flrg module¶

class pyFTS.models.multivariate.flrg.FLRG(**kwargs)[source]¶

Bases: pyFTS.common.flrg.FLRG

Multivariate Fuzzy Logical Rule Group

append_rhs(fset, **kwargs)[source]¶

get_membership(data, variables)[source]¶

Returns the membership value of the FLRG for the input data

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

set_lhs(var, fset)[source]¶

pyFTS.models.multivariate.mvfts module¶

class pyFTS.models.multivariate.mvfts.MVFTS(**kwargs)[source]¶

Bases: pyFTS.common.fts.FTS

Multivariate extension of Chen’s ConventionalFTS method

append_variable(var)[source]¶

Append a new endogenous variable to the model

Parameters:	var – variable object
Returns:

apply_transformations(data, params=None, updateUoD=False, **kwargs)[source]¶

Apply the data transformations for data preprocessing

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

clone_parameters(model)[source]¶

Import the parameters values from other model

Parameters:	model –

forecast(data, **kwargs)[source]¶

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

format_data(data)[source]¶

generate_flrg(flrs)[source]¶

generate_flrs(data)[source]¶

generate_lhs_flrs(data)[source]¶

train(data, **kwargs)[source]¶

Method specific parameter fitting

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

pyFTS.models.multivariate.variable module¶

class pyFTS.models.multivariate.variable.Variable(name, **kwargs)[source]¶

Bases: object

A variable of a fuzzy time series multivariate model. Each variable contains its own transformations and partitioners.

alias = None¶: A string with the alias of the variable

apply_inverse_transformations(data, **kwargs)[source]¶

apply_transformations(data, **kwargs)[source]¶

build(**kwargs)[source]¶

Parameters:	kwargs –
Returns:

data_label = None¶: A string with the column name on DataFrame

name = None¶: A string with the name of the variable

pyFTS.models.multivariate.wmvfts module¶

class pyFTS.models.multivariate.mvfts.MVFTS(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Multivariate extension of Chen’s ConventionalFTS method

append_variable(var)[source]

Append a new endogenous variable to the model

Parameters:	var – variable object
Returns:

apply_transformations(data, params=None, updateUoD=False, **kwargs)[source]

Apply the data transformations for data preprocessing

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

clone_parameters(model)[source]

Import the parameters values from other model

Parameters:	model –

forecast(data, **kwargs)[source]

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

format_data(data)[source]

generate_flrg(flrs)[source]

generate_flrs(data)[source]

generate_lhs_flrs(data)[source]

train(data, **kwargs)[source]