pyFTS.models.nonstationary package

Submodules

pyFTS.models.nonstationary.common module

Non Stationary Fuzzy Sets

GARIBALDI, Jonathan M.; JAROSZEWSKI, Marcin; MUSIKASUWAN, Salang. Nonstationary fuzzy sets. IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.

class pyFTS.models.nonstationary.common.FuzzySet(name, mf, parameters, **kwargs)

Bases: pyFTS.common.FuzzySet.FuzzySet

Non Stationary Fuzzy Sets

get_lower(t)

get_midpoint(t)

get_upper(t)

membership(x, t)

Calculate the membership value of a given input

Parameters:

• x – input value
• t – time displacement or perturbation parameters

Returns:

membership value of x at this fuzzy set

perform_location(t, param)

perform_width(t, param)

perturbate_parameters(t)

pyFTS.models.nonstationary.common.check_bounds(data, partitioner, t)

pyFTS.models.nonstationary.common.check_bounds_index(data, partitioner, t)

pyFTS.models.nonstationary.common.fuzzify(inst, t, fuzzySets)

Calculate the membership values for a data point given nonstationary fuzzy sets

Parameters:

• inst – data points
• t – time displacement of the instance
• fuzzySets – list of fuzzy sets

Returns:

array of membership values

pyFTS.models.nonstationary.common.fuzzySeries(data, fuzzySets, ordered_sets, window_size=1, method='fuzzy', const_t=None)

pyFTS.models.nonstationary.common.window_index(t, window_size)

pyFTS.models.nonstationary.cvfts module

class pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS(**kwargs)

Bases: pyFTS.models.hofts.HighOrderFTS

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_interval(ndata, **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

generate_flrg(flrs, **kwargs)

perturbation_factors(data, **kwargs)

perturbation_factors__old(data)

train(ndata, **kwargs)

Method specific parameter fitting

Parameters:

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

class pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG(order, **kwargs)

Bases: pyFTS.models.hofts.HighOrderFTS

Conventional High Order Fuzzy Logical Relationship Group

append_lhs(c)

append_rhs(c, **kwargs)

pyFTS.models.nonstationary.flrg module

class pyFTS.models.nonstationary.flrg.NonStationaryFLRG(LHS, **kwargs)

Bases: pyFTS.common.flrg.FLRG

get_key()

Returns a unique identifier for this FLRG

get_lower(*args)

Returns the lower bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:lower bound value

get_membership(data, *args)

Returns the membership value of the FLRG for the input data

Parameters:

• data – input data
• sets – fuzzy sets

Returns:

the membership value

get_midpoint(*args)

Returns the midpoint value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:the midpoint value

get_upper(*args)

Returns the upper bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:upper bound value

unpack_args(*args)

pyFTS.models.nonstationary.honsfts module

class pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG(order, **kwargs)

Bases: pyFTS.models.nonstationary.flrg.NonStationaryFLRG

First Order NonStationary Fuzzy Logical Relationship Group

append_lhs(c)

append_rhs(fset, **kwargs)

get_lower(sets, perturb)

Returns the lower bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:lower bound value

get_midpoint(sets, perturb)

Returns the midpoint value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:the midpoint value

get_upper(sets, perturb)

Returns the upper bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:upper bound value

weights()

class pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS(**kwargs)

Bases: pyFTS.models.nonstationary.nsfts.NonStationaryFTS

NonStationaryFTS Fuzzy Time Series

configure_lags(**kwargs)

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

generate_flrg(data, **kwargs)

train(data, **kwargs)

Method specific parameter fitting

Parameters:

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

pyFTS.models.nonstationary.nsfts module

class pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG(LHS, **kwargs)

Bases: pyFTS.models.nonstationary.flrg.NonStationaryFLRG

First Order NonStationary Fuzzy Logical Relationship Group

append_rhs(c, **kwargs)

get_key()

Returns a unique identifier for this FLRG

class pyFTS.models.nonstationary.nsfts.NonStationaryFTS(**kwargs)

Bases: pyFTS.common.fts.FTS

NonStationaryFTS Fuzzy Time Series

conditional_perturbation_factors(data, **kwargs)

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_interval(ndata, **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

generate_flrg(flrs, **kwargs)

train(data, **kwargs)

Method specific parameter fitting

Parameters:

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

class pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG(LHS, **kwargs)

Bases: pyFTS.models.nonstationary.flrg.NonStationaryFLRG

First Order NonStationary Fuzzy Logical Relationship Group

append_rhs(c, **kwargs)

get_key()

Returns a unique identifier for this FLRG

get_midpoint(sets, perturb)

Returns the midpoint value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:the midpoint value

weights()

class pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS(**kwargs)

Bases: pyFTS.models.nonstationary.nsfts.NonStationaryFTS

Weighted NonStationaryFTS Fuzzy Time Series

generate_flrg(flrs, **kwargs)

train(data, **kwargs)

Method specific parameter fitting

Parameters:

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

pyFTS.models.nonstationary.partitioners module

class pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner(data, part, **kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Non Stationary Universe of Discourse Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

get_polynomial_perturbations(data, **kwargs)

poly_width(par1, par2, rng, deg)

scale_down(x, pct)

scale_up(x, pct)

class pyFTS.models.nonstationary.partitioners.SimpleNonStationaryPartitioner(data, part, **kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Non Stationary Universe of Discourse Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.models.nonstationary.partitioners.simplenonstationary_gridpartitioner_builder(data, npart, transformation)

pyFTS.models.nonstationary.perturbation module

Pertubation functions for Non Stationary Fuzzy Sets

pyFTS.models.nonstationary.perturbation.exponential(x, parameters)

pyFTS.models.nonstationary.perturbation.linear(x, parameters)

pyFTS.models.nonstationary.perturbation.periodic(x, parameters)

pyFTS.models.nonstationary.perturbation.polynomial(x, parameters)

pyFTS.models.nonstationary.util module

pyFTS.models.nonstationary.util.plot_sets(partitioner, start=0, end=10, step=1, tam=[5, 5], colors=None, save=False, file=None, axes=None, data=None, window_size=1, only_lines=False)

pyFTS.models.nonstationary.util.plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, save=False, file=None, axes=None, fig=None)

Module contents

Fuzzy time series with nonstationary fuzzy sets, for heteroskedastic data