pyFTS.models.ensemble package

Submodules

pyFTS.models.ensemble.ensemble module

EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point, interval and probabilistic forecasting.

Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.

class pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS(**kwargs)[source]

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS

Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner

set_transformations(model)[source]
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters:
  • data – training time series data
  • kwargs – Method specific parameters
class pyFTS.models.ensemble.ensemble.EnsembleFTS(**kwargs)[source]

Bases: pyFTS.common.fts.FTS

Ensemble FTS

alpha = None

The quantiles

append_model(model)[source]

Append a new trained model to the ensemble

Parameters:model – FTS 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_distribution(data, steps, **kwargs)[source]

Probabilistic 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
  • kwargs – model specific parameters
Returns:

a list with the forecasted Probability Distributions
forecast_ahead_interval(data, steps, **kwargs)[source]

Interval 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
  • kwargs – model specific parameters
Returns:

a list with the forecasted intervals
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
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
get_distribution_interquantile(forecasts, alpha)[source]
get_interval(forecasts)[source]
get_models_forecasts(data)[source]
get_point(forecasts, **kwargs)[source]
interval_method = None

The method used to mix the several model’s forecasts into a interval forecast. Options: quantile, extremum, normal

models = None

A list of FTS models, the ensemble components

parameters = None

A list with the parameters for each component model

point_method = None

The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile, exponential

train(data, **kwargs)[source]

Method specific parameter fitting

Parameters:
  • data – training time series data
  • kwargs – Method specific parameters
pyFTS.models.ensemble.ensemble.sampler(data, quantiles)[source]

pyFTS.models.ensemble.multiseasonal module

Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.

class pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS(name, **kwargs)[source]

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS

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
train(data, **kwargs)[source]

Method specific parameter fitting

Parameters:
  • data – training time series data
  • kwargs – Method specific parameters
update_uod(data)[source]
pyFTS.models.ensemble.multiseasonal.train_individual_model(partitioner, train_data, indexer)[source]

Module contents

Meta FTS that aggregates other FTS methods