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¶

class pyFTS.models.multivariate.common.MultivariateFuzzySet(**kwargs)[source]¶

Bases: pyFTS.common.Composite.FuzzySet

Multivariate Composite Fuzzy Set

append_set(variable, set)[source]¶

Appends a new fuzzy set from a new variable

Parameters:	variable – an multivariate.variable instance set – an common.FuzzySet instance

membership(x)[source]¶

Calculate the membership value of a given input

Parameters:	x – input value
Returns:	membership value of x at this fuzzy set

set_target_variable(variable)[source]¶

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

pyFTS.models.multivariate.common.fuzzyfy_instance_clustered(data_point, cluster, **kwargs)[source]¶

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

alpha_cut = None¶: Minimal membership value to be considered on fuzzyfication process

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

data_type = None¶: The type of the data column on Pandas Dataframe

mask = None¶: The mask for format the data column on Pandas Dataframe

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

partitioner = None¶: UoD partitioner for the variable data

transformation = None¶: Pre processing transformation for the variable

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_lower(sets)[source]¶

Returns the lower bound value for the RHS fuzzy sets

Parameters:	sets – fuzzy sets
Returns:	lower bound value

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

get_upper(sets)[source]¶

Returns the upper bound value for the RHS fuzzy sets

Parameters:	sets – fuzzy sets
Returns:	upper bound 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

forecast_ahead(data, steps, **kwargs)[source]¶

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

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

Interval 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 prediction intervals

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.mvfts.product_dict(**kwargs)[source]¶: Code by Seth Johnson :param kwargs: :return:

pyFTS.models.multivariate.wmvfts module¶

class pyFTS.models.multivariate.wmvfts.WeightedFLRG(**kwargs)[source]¶

Bases: pyFTS.models.multivariate.flrg.FLRG

Weighted Multivariate Fuzzy Logical Rule Group

append_rhs(fset, **kwargs)[source]¶

get_lower(sets)[source]¶

Returns the lower bound value for the RHS fuzzy sets

Parameters:	sets – fuzzy sets
Returns:	lower bound value

get_midpoint(sets)[source]¶

Returns the midpoint value for the RHS fuzzy sets

Parameters:	sets – fuzzy sets
Returns:	the midpoint value

get_upper(sets)[source]¶

Returns the upper bound value for the RHS fuzzy sets

Parameters:	sets – fuzzy sets
Returns:	upper bound value

weights()[source]¶

class pyFTS.models.multivariate.wmvfts.WeightedMVFTS(**kwargs)[source]¶

Bases: pyFTS.models.multivariate.mvfts.MVFTS

Weighted Multivariate FTS

generate_flrg(flrs)[source]¶

pyFTS.models.multivariate.cmvfts module¶

class pyFTS.models.multivariate.cmvfts.ClusteredMVFTS(**kwargs)[source]¶

Bases: pyFTS.models.multivariate.mvfts.MVFTS

Meta model for multivariate, high order, clustered multivariate FTS

check_data(data)[source]¶

forecast(ndata, **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

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

Multivariate forecast one step ahead

Parameters:	data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model kwargs – model specific parameters
Returns:	a Pandas Dataframe object representing the forecasted values for each variable

fts_method = None¶: The FTS method to be called when a new model is build

fts_params = None¶: The FTS method specific parameters

fuzzyfy(data)[source]¶

model = None¶: The most recent trained model