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`	Baseline model that sets the forecast equal to the last observed value.
`SeasonalNaiveModel`	Baseline model that sets the forecast equal to the last observed value from the same season.
`ARIMAModel`	Autoregressive Integrated Moving Average (ARIMA) model.
`ETSModel`	Exponential smoothing with trend and seasonality.
`ThetaModel`	The Theta forecasting model of Assimakopoulos and Nikolopoulos (2000).
`AutoETSModel`	Automatically tuned exponential smoothing with trend and seasonality.
`AutoARIMAModel`	Automatically tuned ARIMA model.
`DynamicOptimizedThetaModel`	Optimized Theta forecasting model from Fiorucci et al. (2016).
`AutoGluonTabularModel`	Predict future time series values using autogluon.tabular.TabularPredictor.
`DeepARModel`	DeepAR model from GluonTS based on the PyTorch backend.
`SimpleFeedForwardModel`	SimpleFeedForward model from GluonTS based on the PyTorch backend.
`TemporalFusionTransformerModel`	TemporalFusionTransformer model from GluonTS.

NaiveModel#

class autogluon.timeseries.models.NaiveModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Baseline model that sets the forecast equal to the last observed value.

Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is estimated from the empirical distribution of the residuals. As described in https://otexts.com/fpp3/prediction-intervals.html

SeasonalNaiveModel#

class autogluon.timeseries.models.SeasonalNaiveModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Baseline model that sets the forecast equal to the last observed value from the same season.

Parameters: seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, will fall back to Naive forecast. Seasonality will also be disabled, if the length of the time series is < seasonal_period.

ARIMAModel#

class autogluon.timeseries.models.ARIMAModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Autoregressive Integrated Moving Average (ARIMA) model.

Based on statsmodels.tsa.statespace.sarimax.SARIMAX.

Our implementation contains several improvements over the Statsmodels version, such as multi-CPU training and reducing the disk usage when saving models.

Parameters

order (Tuple[int, int, int], default = (1, 1, 1)) – The (p, d, q) order of the model for the number of AR parameters, differences, and MA parameters to use.
seasonal_order (Tuple[int, int, int], default = (0, 0, 0)) – The (P, D, Q) parameters of the seasonal ARIMA model. Setting to (0, 0, 0) disables seasonality.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
trend ({"n", "c", "t", "ct"}, default = "c") – Parameter controlling the trend polynomial. Allowed values are “n” (no trend), “c” (constant), “t” (linear) and “ct” (constant plus linear).
enforce_stationarity (bool, default = True) – Whether to transform the AR parameters to enforce stationarity in the autoregressive component of the model. If ARIMA crashes during fitting with an LU decomposition error, you can either set enforce_stationarity to False or increase the differencing parameter d in order.
enforce_invertibility (bool, default = True) – Whether to transform the MA parameters to enforce invertibility in the moving average component of the model.
maxiter (int, default = 50) – Number of iterations during optimization.
n_jobs (int or float, default = 0.5) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

ETSModel#

class autogluon.timeseries.models.ETSModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Exponential smoothing with trend and seasonality.

Based on statsmodels.tsa.exponential_smoothing.ets.ETSModel.

Our implementation contains several improvements over the Statsmodels version, such as multi-CPU training and reducing the disk usage when saving models.

Parameters

error ({"add", "mul"}, default = "add") – Error model. Allowed values are “add” (additive) and “mul” (multiplicative). Note that “mul” is only applicable to time series with positive values.
trend ({"add", "mul", None}, default = "add") – Trend component model. Allowed values are “add” (additive), “mul” (multiplicative) and None (disabled). Note that “mul” is only applicable to time series with positive values.
damped_trend (bool, default = False) – Whether or not the included trend component is damped.
seasonal ({"add", "mul", None}, default = "add") – Seasonal component model. Allowed values are “add” (additive), “mul” (multiplicative) and None (disabled). Note that “mul” is only applicable to time series with positive values.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled. Seasonality will also be disabled, if the length of the time series is < 2 * seasonal_period.
maxiter (int, default = 1000) – Number of iterations during optimization.
n_jobs (int or float, default = 0.5) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

ThetaModel#

class autogluon.timeseries.models.ThetaModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

The Theta forecasting model of Assimakopoulos and Nikolopoulos (2000).

Based on statsmodels.tsa.forecasting.theta.ThetaModel.

Our implementation contains several improvements over the Statsmodels version, such as multi-CPU training and reducing the disk usage when saving models.

References

Assimakopoulos, Vassilis, and Konstantinos Nikolopoulos. “The theta model: a decomposition approach to forecasting.” International journal of forecasting 16.4 (2000): 521-530.

Parameters

deseasonalize (bool, default = True) – Whether to deseasonalize the data. If True and use_test is True, the data is only deseasonalized if the null hypothesis of no seasonality is rejected.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled. Seasonality will also be disabled, if the length of the time series is < 2 * seasonal_period.
use_test (bool, default = True) – Whether to use a statistical test for determining if the seasonality is present.
method ({"auto", "additive", "multiplicative"}, default = "auto") – The model used for the seasonal decomposition. “auto” uses multiplicative if the time series is non-negative and all estimated seasonal components are positive. If either of these conditions is False, then it uses an additive decomposition.
difference (bool, default = False) – Whether to difference the data before testing for seasonality.
n_jobs (int or float, default = 0.5) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

AutoETSModel#

class autogluon.timeseries.models.AutoETSModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Automatically tuned exponential smoothing with trend and seasonality.

Automatically selects the best ETS (Error, Trend, Seasonality) model using an information criterion

Based on statsforecast.models.AutoETS.

Parameters

model (str, default = "ZZZ") – Model string describing the configuration of the E (error), T (trend) and S (seasonal) model components. Each component can be one of “M” (multiplicative), “A” (additive), “N” (omitted). For example when model=”ANN” (additive error, no trend, and no seasonality), ETS will explore only a simple exponential smoothing.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
n_jobs (int or float, default = -1) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

AutoARIMAModel#

class autogluon.timeseries.models.AutoARIMAModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Automatically tuned ARIMA model.

Automatically selects the best (p,d,q,P,D,Q) model parameters using an information criterion

Based on statsforecast.models.AutoARIMA.

Parameters

d (int, optional) – Order of first differencing. If None, will be determined automatically using a statistical test.
D (int, optional) – Order of seasonal differencing. If None, will be determined automatically using a statistical test.
max_p (int, default = 5) – Maximum number of autoregressive terms.
max_q (int, default = 5) – Maximum order of moving average.
max_P (int, default = 2) – Maximum number of seasonal autoregressive terms.
max_Q (int, default = 2) – Maximum order of seasonal moving average.
max_d (int, default = 2) – Maximum order of first differencing.
max_D (int, default = 1) – Maximum order of seasonal differencing.
start_p (int, default = 2) – Starting value of p in stepwise procedure.
start_q (int, default = 2) – Starting value of q in stepwise procedure.
start_P (int, default = 1) – Starting value of P in stepwise procedure.
start_Q (int, default = 1) – Starting value of Q in stepwise procedure.
stationary (bool, default = False) – Restrict search to stationary models.
seasonal (bool, default = True) – Whether to consider seasonal models.
approximation (bool, default = True) – Approximate optimization for faster convergence.
allowdrift (bool, default = False) – If True, drift term is allowed.
allowmean (bool, default = True) – If True, non-zero mean is allowed.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
n_jobs (int or float, default = -1) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

DynamicOptimizedThetaModel#

class autogluon.timeseries.models.DynamicOptimizedThetaModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Optimized Theta forecasting model from Fiorucci et al. (2016).

Based on statsforecast.models.DynamicOptimizedTheta.

References

Fiorucci, Jose et al. “Models for optimising the theta method and their relationship to state space models.” International journal of forecasting 32.4 (2016): 1151-1161.

Parameters

decomposition_type ({"multiplicative", "additive"}, default = "multiplicative") – Seasonal decomposition type.
seasonal_period (int or None, default = None) – Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a weekly cycle or 12 for monthly data with an annual cycle. When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be specified manually by providing an integer > 1. If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
n_jobs (int or float, default = 0.5) – Number of CPU cores used to fit the models in parallel. When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used. When set to a positive integer, that many cores are used. When set to -1, all CPU cores are used.

AutoGluonTabularModel#

class autogluon.timeseries.models.AutoGluonTabularModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

Predict future time series values using autogluon.tabular.TabularPredictor.

The forecasting is converted to a tabular problem using the following features:

lag features (observed time series values) based on freq of the data
time features (e.g., day of the week) based on the timestamp of the measurement
lagged known and past covariates (if available)
static features of each item (if available)

Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is estimated from the empirical distribution of the residuals.

Parameters

max_train_size (int, default = 1_000_000) – Maximum number of rows in the training and validation sets. If the number of rows in train or validation data exceeds max_train_size, then max_train_size many rows are subsampled from the dataframe.
tabular_hyperparameters (Dict[Dict[str, Any]], optional) – Hyperparameters dictionary passed to TabularPredictor.fit. Contains the names of models that should be fit. Defaults to {"XGB": {}, "CAT": {}, "GBM" :{}}.

DeepARModel#

class autogluon.timeseries.models.DeepARModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

DeepAR model from GluonTS based on the PyTorch backend.

The model consists of an LSTM encoder and a decoder that outputs the distribution of the next target value. Close to the model described in [Salinas2020].

Based on gluonts.torch.model.deepar.DeepAREstimator.

References

Salinas2020: Salinas, David, et al. “DeepAR: Probabilistic forecasting with autoregressive recurrent networks.” International Journal of Forecasting. 2020.

Parameters

context_length (int, optional) – Number of steps to unroll the RNN for before computing predictions (default: None, in which case context_length = prediction_length)
disable_static_features (bool, default = False) – If True, static features won’t be used by the model even if they are present in the dataset. If False, static features will be used by the model if they are present in the dataset.
disable_known_covariates (bool, default = False) – If True, known covariates won’t be used by the model even if they are present in the dataset. If False, known covariates will be used by the model if they are present in the dataset.
num_layers (int, default = 2) – Number of RNN layers
hidden_size (int, default = 40) – Number of RNN cells for each layer
dropout_rate (float, default = 0.1) – Dropout regularization parameter
embedding_dimension (int, optional) – Dimension of the embeddings for categorical features (if None, defaults to [min(50, (cat+1)//2) for cat in cardinality])
distr_output (gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()) – Distribution to use to evaluate observations and sample predictions
scaling (bool, default = True) – Whether to automatically scale the target values
epochs (int, default = 100) – Number of epochs the model will be trained for
batch_size (int, default = 64) – Size of batches used during training
num_batches_per_epoch (int, default = 50) – Number of batches processed every epoch
learning_rate (float, default = 1e-3,) – Learning rate used during training

SimpleFeedForwardModel#

class autogluon.timeseries.models.SimpleFeedForwardModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

SimpleFeedForward model from GluonTS based on the PyTorch backend.

The model consists of a multilayer perceptron (MLP) that predicts the distribution of all the target value in the forecast horizon.

Based on gluonts.torch.model.simple_feedforward.SimpleFeedForwardEstimator. See GluonTS documentation for additional hyperparameters.

Parameters

context_length (int, optional) – Number of time units that condition the predictions (default: None, in which case context_length = prediction_length)
hidden_dimensions (List[int], default = [20, 20]) – Size of hidden layers in the feedforward network
distr_output (gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()) – Distribution to fit.
batch_normalization (bool, default = False) – Whether to use batch normalization
mean_scaling (bool, default = True) – Scale the network input by the data mean and the network output by its inverse
epochs (int, default = 100) – Number of epochs the model will be trained for
batch_size (int, default = 64) – Size of batches used during training
num_batches_per_epoch (int, default = 50) – Number of batches processed every epoch
learning_rate (float, default = 1e-3,) – Learning rate used during training

TemporalFusionTransformerModel#

class autogluon.timeseries.models.TemporalFusionTransformerModel(freq: Optional[str] = None, prediction_length: int = 1, path: Optional[str] = None, name: Optional[str] = None, eval_metric: Optional[str] = None, hyperparameters: Optional[Dict[str, Any]] = None, **kwargs)[source]#

TemporalFusionTransformer model from GluonTS.

The model combines an LSTM encoder, a transformer decoder, and directly predicts the quantiles of future target values. As described in [Lim2021].

Based on gluonts.torch.model.tft.TemporalFusionTransformerEstimator. See GluonTS documentation for additional hyperparameters.

References

Lim2021: Lim, Bryan, et al. “Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.” International Journal of Forecasting. 2021.

Parameters

context_length (int, default = 64) – Number of past values used for prediction.
disable_static_features (bool, default = False) – If True, static features won’t be used by the model even if they are present in the dataset. If False, static features will be used by the model if they are present in the dataset.
disable_known_covariates (bool, default = False) – If True, known covariates won’t be used by the model even if they are present in the dataset. If False, known covariates will be used by the model if they are present in the dataset.
disable_past_covariates (bool, default = False) – If True, past covariates won’t be used by the model even if they are present in the dataset. If False, past covariates will be used by the model if they are present in the dataset.
hidden_dim (int, default = 32) – Size of the LSTM & transformer hidden states.
variable_dim (int, default = 32) – Size of the feature embeddings.
num_heads (int, default = 4) – Number of attention heads in self-attention layer in the decoder.
dropout_rate (float, default = 0.1) – Dropout regularization parameter
epochs (int, default = 100) – Number of epochs the model will be trained for
batch_size (int, default = 64) – Size of batches used during training
num_batches_per_epoch (int, default = 50) – Number of batches processed every epoch
learning_rate (float, default = 1e-3,) – Learning rate used during training

MXNet Models#

Following MXNet-based models from GluonTS are available in AutoGluon.

DeepARMXNetModel
MQCNNMXNetModel
MQRNNMXNetModel
SimpleFeedForwardMXNetModel
TemporalFusionTransformerMXNetModel
TransformerMXNetModel

Documentation and hyperparameter settings for these models can be found here.

Using the above models 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.

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)	Past covariates (continuous)
`AutoGluonTabularModel`	✓	✓	✓	✓
`DeepARModel`	✓	✓	✓
`TemporalFusionTransformerModel`	✓	✓	✓	✓
`DeepARMXNetModel`	✓	✓	✓
`MQCNNMXNetModel`	✓	✓	✓	✓
`TemporalFusionTransformerMXNetModel`	✓		✓