TimeSeriesPredictor.fit¶

Fit probabilistic forecasting models to the given time series dataset.

Parameters:

train_data (Union[TimeSeriesDataFrame, pd.DataFrame, Path, str]) –
Training data in the TimeSeriesDataFrame format.

Time series with length <= (num_val_windows + 1) * prediction_length will be ignored during training. See num_val_windows for details.

If known_covariates_names were specified when creating the predictor, train_data must include the columns listed in known_covariates_names with the covariates values aligned with the target time series. The known covariates must have a numeric (float or integer) dtype.

Columns of train_data except target and those listed in known_covariates_names will be interpreted as past_covariates - covariates that are known only in the past.

If train_data has static features (i.e., train_data.static_features is a pandas DataFrame), the predictor will interpret columns with int and float dtypes as continuous (real-valued) features, columns with object and str dtypes as categorical features, and will ignore the rest of columns.

For example, to ensure that column “store_id” with dtype int is interpreted as a category, we need to change its type to category:
```
data.static_features["store_id"] = data.static_features["store_id"].astype("category")
```
If provided data is a path or a pandas.DataFrame, AutoGluon will attempt to automatically convert it to a TimeSeriesDataFrame.
tuning_data (Union[TimeSeriesDataFrame, pd.DataFrame, Path, str], optional) –
Data reserved for model selection and hyperparameter tuning, rather than training individual models. Also used to compute the validation scores. Note that only the last prediction_length time steps of each time series are used for computing the validation score.

If tuning_data is provided, multi-window backtesting on training data will be disabled, the num_val_windows will be set to 0, and refit_full will be set to False.

Leaving this argument empty and letting AutoGluon automatically generate the validation set from train_data is a good default.

If known_covariates_names were specified when creating the predictor, tuning_data must also include the columns listed in known_covariates_names with the covariates values aligned with the target time series.

If train_data has past covariates or static features, tuning_data must have also include them (with same columns names and dtypes).

If provided data is a path or a pandas.DataFrame, AutoGluon will attempt to automatically convert it to a TimeSeriesDataFrame.
time_limit (int, optional) – Approximately how long fit() will run (wall-clock time in seconds). If not specified, fit() will run until all models have completed training.
presets (str, optional) –
Optional preset configurations for various arguments in fit().

Can significantly impact predictive accuracy, memory footprint, inference latency of trained models, and various other properties of the returned predictor. It is recommended to specify presets and avoid specifying most other fit() arguments or model hyperparameters prior to becoming familiar with AutoGluon. For example, set presets="high_quality" to get a high-accuracy predictor, or set presets="fast_training" to quickly get the results. Any user-specified arguments in fit() will override the values used by presets.

Available presets:
- "fast_training": fit simple statistical models (ETS, Theta, Naive, SeasonalNaive) + fast tree-based models RecursiveTabular and DirectTabular. These models are fast to train but may not be very accurate.
- "medium_quality": all models mentioned above + deep learning model TemporalFusionTransformer. Default setting that produces good forecasts with reasonable training time.
- "high_quality": All ML models available in AutoGluon + additional statistical models (NPTS, AutoETS, AutoARIMA, CrostonSBA, DynamicOptimizedTheta). Much more accurate than medium_quality, but takes longer to train.
- "best_quality": Same models as in "high_quality", but performs validation with multiple backtests. Usually better than high_quality, but takes even longer to train.
Available presets with the Chronos model:
- "chronos_{model_size}": where model size is one of tiny,mini,small,base,large. Uses the Chronos pretrained model for zero-shot forecasting. See the documentation for ChronosModel or see Hugging Face for more information. Note that a GPU is required for model sizes small, base and large.
- "chronos": alias for "chronos_small".
- "chronos_ensemble": builds an ensemble of seasonal naive, tree-based and deep learning models with fast inference and "chronos_small".
- "chronos_large_ensemble": builds an ensemble of seasonal naive, tree-based and deep learning models with fast inference and "chronos_large".
Details for these presets can be found in autogluon/timeseries/configs/presets_configs.py. If not provided, user-provided values for hyperparameters and hyperparameter_tune_kwargs will be used (defaulting to their default values specified below).
hyperparameters (str or dict, optional) –
Determines what models are trained and what hyperparameters are used by each model.

If str is passed, will use a preset hyperparameter configuration defined in autogluon/timeseries/trainer/models/presets.py. Supported values are "default", "light" and "very_light".

If dict is provided, the keys are strings or types that indicate which models to train. Each value is itself a dict containing hyperparameters for each of the trained models, or a list of such dicts. Any omitted hyperparameters not specified here will be set to default. For example:
```
predictor.fit(
    ...
    hyperparameters={
        "DeepAR": {},
        "Theta": [
            {"decomposition_type": "additive"},
            {"seasonal_period": 1},
        ],
    }
)
```
The above example will train three models:
- DeepAR with default hyperparameters
- Theta with additive seasonal decomposition (all other parameters set to their defaults)
- Theta with seasonality disabled (all other parameters set to their defaults)
Full list of available models and their hyperparameters is provided in Forecasting Time Series - Model Zoo.

The hyperparameters for each model can be fixed values (as shown above), or search spaces over which hyperparameter optimization is performed. A search space should only be provided when hyperparameter_tune_kwargs is given (i.e., hyperparameter-tuning is utilized). For example:
```
from autogluon.common import space

predictor.fit(
    ...
    hyperparameters={
        "DeepAR": {
            "hidden_size": space.Int(20, 100),
            "dropout_rate": space.Categorical(0.1, 0.3),
        },
    },
    hyperparameter_tune_kwargs="auto",
)
```
In the above example, multiple versions of the DeepAR model with different values of the parameters “hidden_size” and “dropout_rate” will be trained.
hyperparameter_tune_kwargs (str or dict, optional) –
Hyperparameter tuning strategy and kwargs (for example, how many HPO trials to run). If None, then hyperparameter tuning will not be performed.

If type is str, then this argument specifies a preset. Valid preset values:
- ”auto”: Performs HPO via bayesian optimization search on GluonTS-backed neural forecasting models and random search on other models using local scheduler.
- ”random”: Performs HPO via random search.
You can also provide a dict to specify searchers and schedulers Valid keys:
- ”num_trials”: How many HPO trials to run
- ”scheduler”: Which scheduler to use. Valid values:
  - ”local”: Local shceduler that schedules trials FIFO
- ”searcher”: Which searching algorithm to use. Valid values:
  - ”local_random”: Uses the “random” searcher
  - ”random”: Perform random search
  - ”bayes”: Perform HPO with HyperOpt on GluonTS-backed models via Ray tune. Perform random search on other models.
  - ”auto”: alias for “bayes”
The “scheduler” and “searcher” key are required when providing a dict.

Example:
```
predictor.fit(
    ...
    hyperparameter_tune_kwargs={
        "num_trials": 5,
        "searcher": "auto",
        "scheduler": "local",
    },
)
```
excluded_model_types (List[str], optional) –
Banned subset of model types to avoid training during fit(), even if present in hyperparameters. For example, the following code will train all models included in the high_quality presets except DeepAR:
```
predictor.fit(
    ...,
    presets="high_quality",
    excluded_model_types=["DeepAR"],
)
```
num_val_windows (int, default = 1) –
Number of backtests done on train_data for each trained model to estimate the validation performance. If num_val_windows > 1 is provided, this value may be automatically reduced to ensure that the majority of time series in train_data are long enough for the chosen number of backtests.

Increasing this parameter increases the training time roughly by a factor of num_val_windows // refit_every_n_windows. See refit_every_n_windows and val_step_size: for details.

For example, for prediction_length=2, num_val_windows=3 and val_step_size=1 the folds are:
```
|-------------------|
| x x x x x y y - - |
| x x x x x x y y - |
| x x x x x x x y y |
```
where x are the train time steps and y are the validation time steps.

This argument has no effect if tuning_data is provided.
val_step_size (int or None, default = None) –
Step size between consecutive validation windows. If set to None, defaults to prediction_length provided when creating the predictor.

This argument has no effect if tuning_data is provided.
refit_every_n_windows (int or None, default = 1) –
When performing cross validation, each model will be retrained every refit_every_n_windows validation windows, where the number of validation windows is specified by num_val_windows. Note that in the default setting where num_val_windows=1, this argument has no effect.

If set to None, models will only be fit once for the first (oldest) validation window. By default, refit_every_n_windows=1, i.e., all models will be refit for each validation window.
refit_full (bool, default = False) – If True, after training is complete, AutoGluon will attempt to re-train all models using all of training data (including the data initially reserved for validation). This argument has no effect if tuning_data is provided.
enable_ensemble (bool, default = True) – If True, the TimeSeriesPredictor will fit a simple weighted ensemble on top of the models specified via hyperparameters.
skip_model_selection (bool, default = False) – If True, predictor will not compute the validation score. For example, this argument is useful if we want to use the predictor as a wrapper for a single pre-trained model. If set to True, then the hyperparameters dict must contain exactly one model without hyperparameter search spaces or an exception will be raised.
random_seed (int or None, default = 123) – If provided, fixes the seed of the random number generator for all models. This guarantees reproducible results for most models (except those trained on GPU because of the non-determinism of GPU operations).
verbosity (int, optional) – If provided, overrides the verbosity value used when creating the TimeSeriesPredictor. See documentation for TimeSeriesPredictor for more details.