pyFTS.models.ensemble package¶
Submodules¶
pyFTS.models.ensemble.ensemble module¶
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class
pyFTS.models.ensemble.ensemble.
EnsembleFTS
(**kwargs)[source]¶ Bases:
pyFTS.common.fts.FTS
Ensemble 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 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
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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
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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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models
= None¶ A list of FTS models, the ensemble components
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parameters
= None¶ A list with the parameters for each component model
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pyFTS.models.ensemble.multiseasonal module¶
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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