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)

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS

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

set_transformations(model)
train(data, **kwargs)

Method specific parameter fitting

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

Bases: pyFTS.common.fts.FTS

Ensemble FTS

append_model(model)

Append a new trained model to the ensemble

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

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
  • 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
forecast_ahead_interval(data, steps, **kwargs)

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
  • 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
forecast_distribution(data, **kwargs)

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)

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_UoD()

Returns the interval of the known bounds of the universe of discourse (UoD), i. e., the known minimum and maximum values of the time series.

Returns:A set with the lower and the upper bounds of the UoD
get_distribution_interquantile(forecasts, alpha)
get_interval(forecasts)
get_models_forecasts(data)
get_point(forecasts, **kwargs)
train(data, **kwargs)

Method specific parameter fitting

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

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS

An homogeneous FTS method ensemble with variations on partitionings and orders.

train(data, **kwargs)

Method specific parameter fitting

Parameters:
  • data – training time series data
  • kwargs – Method specific parameters
pyFTS.models.ensemble.ensemble.sampler(data, quantiles, bounds=False)

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)

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS

forecast_distribution(data, **kwargs)

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)

Method specific parameter fitting

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

Module contents

Meta FTS that aggregates other FTS methods