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.
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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
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class
pyFTS.models.ensemble.ensemble.
EnsembleFTS
(**kwargs)[source]¶ Bases:
pyFTS.common.fts.FTS
Ensemble FTS
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alpha
¶ The quantiles
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append_model
(model)[source]¶ Append a new trained model to the ensemble
- Parameters
model – FTS model
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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_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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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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get_UoD
()[source]¶ 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
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interval_method
¶ The method used to mix the several model’s forecasts into a interval forecast. Options: quantile, extremum, normal
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models
¶ A list of FTS models, the ensemble components
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parameters
¶ A list with the parameters for each component model
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point_method
¶ The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile, exponential
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class
pyFTS.models.ensemble.ensemble.
SimpleEnsembleFTS
(**kwargs)[source]¶ Bases:
pyFTS.models.ensemble.ensemble.EnsembleFTS
An homogeneous FTS method ensemble with variations on partitionings and orders.
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method
¶ FTS method class that will be used on internal models
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orders
¶ Possible variations of order on internal models
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partitioner_method
¶ UoD partitioner class that will be used on internal methods
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partitions
¶ Possible variations of number of partitions on internal models
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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.
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class
pyFTS.models.ensemble.multiseasonal.
SeasonalEnsembleFTS
(name, **kwargs)[source]¶ Bases:
pyFTS.models.ensemble.ensemble.EnsembleFTS
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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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Module contents¶
Meta FTS that aggregates other FTS methods