TimeSeriesPredictor.predict¶
- TimeSeriesPredictor.predict(data: TimeSeriesDataFrame | DataFrame | Path | str, known_covariates: TimeSeriesDataFrame | DataFrame | Path | str | None = None, model: str | None = None, use_cache: bool = True, random_seed: int | None = 123) TimeSeriesDataFrame [source]¶
Return quantile and mean forecasts for the given dataset, starting from the end of each time series.
- Parameters:
data (Union[TimeSeriesDataFrame, pd.DataFrame, Path, str]) –
Time series data to forecast with.
If
known_covariates_names
were specified when creating the predictor,data
must include the columns listed inknown_covariates_names
with the covariates values aligned with the target time series.If
train_data
used to train the predictor contained past covariates or static features, thendata
must also include them (with same column names and dtypes).If provided data is an instance of pandas DataFrame, AutoGluon will attempt to automatically convert it to a
TimeSeriesDataFrame
.known_covariates (Union[TimeSeriesDataFrame, pd.DataFrame, Path, str], optional) –
If
known_covariates_names
were specified when creating the predictor, it is necessary to provide the values of the known covariates for each time series during the forecast horizon. That is:The columns must include all columns listed in
known_covariates_names
The
item_id
index must include all item ids present indata
The
timestamp
index must include the values forprediction_length
many time steps into the future from the end of each time series indata
See example below.
model (str, optional) – Name of the model that you would like to use for prediction. By default, the best model during training (with highest validation score) will be used.
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).
use_cache (bool, default = True) – If True, will attempt to use the cached predictions. If False, cached predictions will be ignored. This argument is ignored if
cache_predictions
was set to False when creating theTimeSeriesPredictor
.
Examples
>>> print(data) target promotion price item_id timestamp A 2020-01-05 20 0 19.9 2020-01-06 40 1 9.9 2020-01-07 32 0 15.0 B 2020-03-01 13 0 5.0 2020-03-02 44 1 2.9 2020-03-03 72 1 2.9 >>> predictor = TimeSeriesPredictor(prediction_length=2, known_covariates_names=["promotion", "price"]).fit(data) >>> print(future_known_covariates) promotion price item_id timestamp A 2020-01-08 1 12.9 2020-01-09 1 12.9 B 2020-03-04 0 5.0 2020-03-05 0 7.0 >>> predictor.predict(data, known_covariates=future_known_covariates) mean item_id timestamp A 2020-01-08 30.2 2020-01-09 27.0 B 2020-03-04 17.1 2020-03-05 8.3