actableai.timeseries.models.ext package

Submodules

actableai.timeseries.models.ext.r module

class actableai.timeseries.models.ext.r.RForecastPredictor(freq: str, prediction_length: int, method_name: str = 'ets', period: Optional[int] = None, trunc_length: Optional[int] = None, params: Optional[Dict] = None)

Bases: gluonts.model.predictor.RepresentablePredictor

Wrapper for calling the `R forecast package.

<http://pkg.robjhyndman.com/forecast/>`_.

The RForecastPredictor is a thin wrapper for calling the R forecast package. In order to use it you need to install R and run:

pip install 'rpy2>=2.9.*,<3.*'
R -e 'install.packages(c("forecast", "nnfor"), repos="https://cloud.r-project.org")'

Parameters

• freq – The granularity of the time series (e.g. ‘1H’)
• prediction_length – Number of time points to be predicted.
• method – The method from rforecast to be used one of “ets”, “arima”, “tbats”, “croston”, “mlp”, “thetaf”.
• period – The period to be used (this is called frequency in the R forecast package), result to a tentative reasonable default if not specified (for instance 24 for hourly freq ‘1H’)
• trunc_length – Maximum history length to feed to the model (some models become slow with very long series).
• params – Parameters to be used when calling the forecast method default. Note that currently only output_type = ‘samples’ is supported.

predict(dataset: gluonts.dataset.Dataset, num_samples: int = 100, intervals: Optional[List] = None, save_info: bool = False, **kwargs) → Iterator[Union[gluonts.model.forecast.SampleForecast, gluonts.model.forecast.QuantileForecast]]

Compute forecasts for the time series in the provided dataset. This method is not implemented in this abstract class; please use one of the subclasses. :param dataset: The dataset containing the time series to predict.

Returns

Iterator over the forecasts, in the same order as the dataset iterable was provided.

Return type

Iterator[Forecast]

actableai.timeseries.models.ext.sktime module

Module contents