pyFTS.models.multivariate package¶
Module contents¶
Multivariate Fuzzy Time Series methods
Submodules¶
pyFTS.models.multivariate.FLR module¶
pyFTS.models.multivariate.common module¶
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class
pyFTS.models.multivariate.common.
MultivariateFuzzySet
(**kwargs)[source]¶ Bases:
pyFTS.common.Composite.FuzzySet
Multivariate Composite Fuzzy Set
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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
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pyFTS.models.multivariate.variable module¶
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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.
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alias
¶ A string with the alias of the variable
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alpha_cut
¶ Minimal membership value to be considered on fuzzyfication process
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data_label
¶ A string with the column name on DataFrame
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data_type
¶ The type of the data column on Pandas Dataframe
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mask
¶ The mask for format the data column on Pandas Dataframe
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name
¶ A string with the name of the variable
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partitioner
¶ UoD partitioner for the variable data
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transformation
¶ Pre processing transformation for the variable
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pyFTS.models.multivariate.flrg module¶
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class
pyFTS.models.multivariate.flrg.
FLRG
(**kwargs)[source]¶ Bases:
pyFTS.common.flrg.FLRG
Multivariate Fuzzy Logical Rule Group
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get_lower
(sets)[source]¶ Returns the lower bound value for the RHS fuzzy sets
- Parameters
sets – fuzzy sets
- Returns
lower bound value
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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
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pyFTS.models.multivariate.partitioner module¶
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class
pyFTS.models.multivariate.partitioner.
MultivariatePartitioner
(**kwargs)[source]¶ Bases:
pyFTS.partitioners.partitioner.Partitioner
Base class for partitioners which use the MultivariateFuzzySet
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build
(data)[source]¶ Perform the partitioning of the Universe of Discourse
- Parameters
data – training data
- Returns
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fuzzyfy
(data, **kwargs)[source]¶ 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
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search
(data, **kwargs)[source]¶ Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several overlapped fuzzy sets.
- Parameters
data – the value to search for the nearest fuzzy sets
type – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.
- Returns
a list with the nearest fuzzy sets
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pyFTS.models.multivariate.grid module¶
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class
pyFTS.models.multivariate.grid.
GridCluster
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.partitioner.MultivariatePartitioner
A cartesian product of all fuzzy sets of all variables
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class
pyFTS.models.multivariate.grid.
IncrementalGridCluster
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.partitioner.MultivariatePartitioner
Create combinations of fuzzy sets of the variables on demand, incrementally increasing the multivariate fuzzy set base.
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fuzzyfy
(data, **kwargs)[source]¶ 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
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pyFTS.models.multivariate.mvfts module¶
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class
pyFTS.models.multivariate.mvfts.
MVFTS
(**kwargs)[source]¶ Bases:
pyFTS.common.fts.FTS
Multivariate extension of Chen’s ConventionalFTS method
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append_variable
(var)[source]¶ Append a new endogenous variable to the model
- Parameters
var – variable object
- Returns
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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
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clone_parameters
(model)[source]¶ Import the parameters values from other model
- Parameters
model – a model to clone the parameters
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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
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forecast_ahead
(data, steps, **kwargs)[source]¶ Point forecast from 1 to H steps ahead, where H is given by the steps parameter
- 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 (default: 1)
start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
- Returns
a list with the forecasted values
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forecast_ahead_interval
(data, steps, **kwargs)[source]¶ Interval forecast from 1 to H steps ahead, where H is given by the steps parameter
- 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_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
- Returns
a list with the forecasted intervals
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pyFTS.models.multivariate.wmvfts module¶
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class
pyFTS.models.multivariate.wmvfts.
WeightedFLRG
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.flrg.FLRG
Weighted Multivariate Fuzzy Logical Rule Group
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get_lower
(sets)[source]¶ Returns the lower bound value for the RHS fuzzy sets
- Parameters
sets – fuzzy sets
- Returns
lower bound value
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get_midpoint
(sets)[source]¶ Returns the midpoint value for the RHS fuzzy sets
- Parameters
sets – fuzzy sets
- Returns
the midpoint value
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class
pyFTS.models.multivariate.wmvfts.
WeightedMVFTS
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.mvfts.MVFTS
Weighted Multivariate FTS
pyFTS.models.multivariate.cmvfts module¶
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class
pyFTS.models.multivariate.cmvfts.
ClusteredMVFTS
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.mvfts.MVFTS
Meta model for high order, clustered multivariate FTS
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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
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forecast_ahead_distribution
(data, steps, **kwargs)[source]¶ Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter
- 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_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
- Returns
a list with the forecasted Probability Distributions
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forecast_ahead_multivariate
(data, steps, **kwargs)[source]¶ 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
start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
- Returns
a Pandas Dataframe object representing the forecasted values for each variable
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forecast_distribution
(data, **kwargs)[source]¶ Probabilistic 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 probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions
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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
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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
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fts_method
¶ The FTS method to be called when a new model is build
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fts_params
¶ The FTS method specific parameters
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model
¶ The most recent trained model
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pyFTS.models.multivariate.granular module¶
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class
pyFTS.models.multivariate.granular.
GranularWMVFTS
(**kwargs)[source]¶ Bases:
pyFTS.models.multivariate.cmvfts.ClusteredMVFTS
Granular multivariate weighted high order FTS
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model
¶ The most recent trained model
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