Forecasting Time Series - Model Zoo¶

Note

This documentation is intended for advanced users and may not be comprehensive.

For a stable public API, refer to TimeSeriesPredictor.

This page contains the list of time series forecasting models available in AutoGluon. The available hyperparameters for each model are listed under Other Parameters.

This list is useful if you want to override the default hyperparameters (Manually configuring models) or define custom hyperparameter search spaces (Hyperparameter tuning), as described in the In-depth Tutorial. For example, the following code will train a TimeSeriesPredictor with DeepAR and ETS models with default hyperparameters (and a weighted ensemble on top of them):

predictor = TimeSeriesPredictor().fit(
   train_data,
   hyperparameters={
      "DeepAR": {},
      "ETS": {},
   },
)

Note that we don’t include the Model suffix when specifying the model name in hyperparameters (e.g., the class DeepARModel corresponds to the name "DeepAR" in the hyperparameters dictionary).

Also note that some of the models’ hyperparameters have names and default values that are different from the original libraries.

Default models¶

NaiveModel¶

SeasonalNaiveModel¶

ARIMAModel¶

ETSModel¶

ThetaModel¶

AutoGluonTabularModel¶

DeepARModel¶

SimpleFeedForwardModel¶

MXNet Models¶

Using the models listed below requires installing Apache MXNet v1.9. This can be done as follows:

python -m pip install mxnet~=1.9

If you want to use a GPU, install the version of MXNet that matches your CUDA version. See the MXNet documentation for more info.

If a GPU is available and MXNet version with CUDA is installed, all the MXNet models will be trained using the GPU. Otherwise, the models will be trained on CPU.

DeepARMXNetModel¶

MQCNNMXNetModel¶

MQRNNMXNetModel¶

SimpleFeedForwardMXNetModel¶

TemporalFusionTransformerMXNetModel¶

TransformerMXNetModel¶

Additional features¶

Overview of the additional features and covariates supported by different models. Models not included in this table currently do not support any additional features.

Model	Static features (continuous)	Static features (categorical)	Known covariates (continuous)
`AutoGluonTabularModel`	✓	✓
`DeepARModel`	✓	✓	✓
`DeepARMXNetModel`	✓	✓	✓
`MQCNNMXNetModel`	✓	✓	✓
`TemporalFusionTransformerMXNetModel`	✓		✓