pyFTS.models.multivariate package

Module contents

Multivariate Fuzzy Time Series methods

Submodules

pyFTS.models.multivariate.FLR module

class pyFTS.models.multivariate.FLR.FLR

Bases: object

Multivariate Fuzzy Logical Relationship

set_lhs(var, set)

set_rhs(set)

pyFTS.models.multivariate.common module

class pyFTS.models.multivariate.common.MultivariateFuzzySet(**kwargs)

Bases: pyFTS.common.Composite.FuzzySet

Multivariate Composite Fuzzy Set

append_set(variable, set)

Appends a new fuzzy set from a new variable

Parameters:

• variable – an multivariate.variable instance
• set – an common.FuzzySet instance

membership(x)

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)

pyFTS.models.multivariate.common.fuzzyfy_instance(data_point, var, tuples=True)

pyFTS.models.multivariate.common.fuzzyfy_instance_clustered(data_point, cluster, **kwargs)

pyFTS.models.multivariate.variable module

class pyFTS.models.multivariate.variable.Variable(name, **kwargs)

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)

apply_transformations(data, **kwargs)

build(**kwargs)

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)

Bases: pyFTS.common.flrg.FLRG

Multivariate Fuzzy Logical Rule Group

append_rhs(fset, **kwargs)

get_lower(sets)

Returns the lower bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:lower bound value

get_membership(data, variables)

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)

Returns the upper bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:upper bound value

set_lhs(var, fset)

pyFTS.models.multivariate.partitioner module

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class pyFTS.models.multivariate.grid.GridCluster(**kwargs)

Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

A cartesian product of all fuzzy sets of all variables

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

class pyFTS.models.multivariate.grid.IncrementalGridCluster(**kwargs)

Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

Create combinations of fuzzy sets of the variables on demand, incrementally increasing the multivariate fuzzy set base.

fuzzyfy(data, **kwargs)

Fuzzyfy the input data according to this partitioner fuzzy sets.

Parameters:

• data – input value to be fuzzyfied
• alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
• method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
• mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership

values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )

:returns a list with the fuzzyfied values, depending on the mode

incremental_search(data, **kwargs)

prune()

pyFTS.models.multivariate.mvfts module

class pyFTS.models.multivariate.mvfts.MVFTS(**kwargs)

Bases: pyFTS.common.fts.FTS

Multivariate extension of Chen’s ConventionalFTS method

append_variable(var)

Append a new endogenous variable to the model

Parameters:var – variable object Returns:

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

Apply the data transformations for data preprocessing

Parameters:

• data – input data
• params – transformation parameters
• updateUoD –
• kwargs –

Returns:

preprocessed data

clone_parameters(model)

Import the parameters values from other model

Parameters:model –

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

forecast_interval(data, **kwargs)

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)

generate_flrg(flrs)

generate_flrs(data)

generate_lhs_flrs(data)

train(data, **kwargs)

Method specific parameter fitting

Parameters:

• data – training time series data
• kwargs – Method specific parameters

pyFTS.models.multivariate.mvfts.product_dict(**kwargs)

Code by Seth Johnson :param kwargs: :return:

pyFTS.models.multivariate.wmvfts module

class pyFTS.models.multivariate.wmvfts.WeightedFLRG(**kwargs)

Bases: pyFTS.models.multivariate.flrg.FLRG

Weighted Multivariate Fuzzy Logical Rule Group

append_rhs(fset, **kwargs)

get_lower(sets)

Returns the lower bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:lower bound value

get_midpoint(sets)

Returns the midpoint value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:the midpoint value

get_upper(sets)

Returns the upper bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:upper bound value

weights()

class pyFTS.models.multivariate.wmvfts.WeightedMVFTS(**kwargs)

Bases: pyFTS.models.multivariate.mvfts.MVFTS

Weighted Multivariate FTS

generate_flrg(flrs)

pyFTS.models.multivariate.cmvfts module

class pyFTS.models.multivariate.cmvfts.ClusteredMVFTS(**kwargs)

Bases: pyFTS.models.multivariate.mvfts.MVFTS

Meta model for high order, clustered multivariate FTS

check_data(data)

forecast(ndata, **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_multivariate(data, steps, **kwargs)

Multivariate forecast n 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
• steps – the number of steps ahead to forecast
• kwargs – model specific parameters

Returns:

a Pandas Dataframe object representing the forecasted values for each variable

forecast_multivariate(data, **kwargs)

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)

model = None

The most recent trained model

train(data, **kwargs)

Method specific parameter fitting

Parameters:

• data – training time series data
• kwargs – Method specific parameters

pyFTS.models.multivariate.granular module

class pyFTS.models.multivariate.granular.GranularWMVFTS(**kwargs)

Bases: pyFTS.models.multivariate.cmvfts.ClusteredMVFTS

Granular multivariate weighted high order FTS

model = None

The most recent trained model

train(data, **kwargs)

Method specific parameter fitting

Parameters:

• data – training time series data
• kwargs – Method specific parameters