Add supports_multivariate property to ForecastingModel

CHANGELOG.md

@@ -13,7 +13,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Improved**
 - General model improvements:
   - Added support for `PathLike` to the `save()` and `load()` functions of all non-deep learning based models. [#1754](https://github.com/unit8co/darts/pull/1754) by [Simon Sudrich](https://github.com/sudrich).
-  - Improved efficiency of `historical_forecasts()` and `backtest()` for all models giving significant process time reduction for larger number of predict iterations and series. [#1801](https://github.com/unit8co/darts/pull/1801) by [Dennis Bader](https://github.com/dennisbader). 
+  - Improved efficiency of `historical_forecasts()` and `backtest()` for all models giving significant process time reduction for larger number of predict iterations and series. [#1801](https://github.com/unit8co/darts/pull/1801) by [Dennis Bader](https://github.com/dennisbader).
+  - Added model property `ForecastingModel.supports_multivariate` to indicate whether the model supports multivariate forecasting. [#1848](https://github.com/unit8co/darts/pull/1848) by [Felix Divo](https://github.com/felixdivo).
 - Improvements to `EnsembleModel`:
   - Model creation parameter `forecasting_models` now supports a mix of `LocalForecastingModel` and `GlobalForecastingModel` (single `TimeSeries` training/inference only, due to the local models). [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).
   - Future and past covariates can now be used even if `forecasting_models` have different covariates support. The covariates passed to `fit()`/`predict()` are used only by models that support it. [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).

darts/models/forecasting/arima.py

@@ -95,6 +95,10 @@ def __init__(
             self._random_state = None
             np.random.seed(random_state if random_state is not None else 0)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None):
         super()._fit(series, future_covariates)
 

darts/models/forecasting/auto_arima.py

@@ -67,6 +67,10 @@ def __init__(
         self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs)
         self.trend = self.model.trend
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None):
         super()._fit(series, future_covariates)
         self._assert_univariate(series)

darts/models/forecasting/baselines.py

@@ -30,6 +30,10 @@ def __init__(self):
         super().__init__()
         self.mean_val = None
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def fit(self, series: TimeSeries):
         super().fit(series)
 
@@ -59,6 +63,10 @@ def __init__(self, K: int = 1):
         self.last_k_vals = None
         self.K = K
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def min_train_series_length(self):
         return max(self.K, 3)
@@ -91,6 +99,10 @@ def __init__(self):
         """
         super().__init__()
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def fit(self, series: TimeSeries):
         super().fit(series)
         assert series.n_samples == 1, "This model expects deterministic time series"
@@ -125,6 +137,10 @@ def __init__(self, input_chunk_length: int = 1):
         self.input_chunk_length = input_chunk_length
         self.rolling_window = None
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def min_train_series_length(self):
         return self.input_chunk_length

darts/models/forecasting/block_rnn_model.py

@@ -321,6 +321,10 @@ def __init__(
         self.n_rnn_layers = n_rnn_layers
         self.dropout = dropout
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module:
         # samples are made of (past_target, past_covariates, future_target)
         input_dim = train_sample[0].shape[1] + (

darts/models/forecasting/croston.py

@@ -98,6 +98,10 @@ def __init__(
 
         self.version = version
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None):
         super()._fit(series, future_covariates)
         self._assert_univariate(series)

darts/models/forecasting/dlinear.py

@@ -452,6 +452,10 @@ def _create_model(
             **self.pl_module_params,
         )
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def supports_static_covariates(self) -> bool:
         return True

darts/models/forecasting/ensemble_model.py

@@ -365,6 +365,10 @@ def _models_are_probabilistic(self) -> bool:
     def _is_probabilistic(self) -> bool:
         return self._models_are_probabilistic()
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return all([model.supports_multivariate for model in self.models])
+
     @property
     def supports_past_covariates(self) -> bool:
         return any([model.supports_past_covariates for model in self.models])

darts/models/forecasting/exponential_smoothing.py

@@ -123,6 +123,10 @@ def predict(self, n, num_samples=1, verbose: bool = False):
 
         return self._build_forecast_series(forecast)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _is_probabilistic(self) -> bool:
         return True
 

darts/models/forecasting/fft.py

@@ -260,6 +260,10 @@ def __init__(
         self.trend = trend
         self.trend_poly_degree = trend_poly_degree
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _exp_trend(self, x) -> Callable:
         """Helper function, used to make FFT model pickable."""
         return np.exp(self.trend_coefficients[1]) * np.exp(

darts/models/forecasting/forecasting_model.py

@@ -195,14 +195,21 @@ def _supports_non_retrainable_historical_forecasts(self) -> bool:
         return False
 
     @property
-    def supports_past_covariates(self):
+    @abstractmethod
+    def supports_multivariate(self) -> bool:
+        """
+        Whether the model considers more than one variate in the time series.
+        """
+
+    @property
+    def supports_past_covariates(self) -> bool:
         """
         Whether model supports past covariates
         """
         return "past_covariates" in inspect.signature(self.fit).parameters.keys()
 
     @property
-    def supports_future_covariates(self):
+    def supports_future_covariates(self) -> bool:
         """
         Whether model supports future covariates
         """
@@ -216,28 +223,28 @@ def supports_static_covariates(self) -> bool:
         return False
 
     @property
-    def uses_past_covariates(self):
+    def uses_past_covariates(self) -> bool:
         """
         Whether the model uses past covariates, once fitted.
         """
         return self._uses_past_covariates
 
     @property
-    def uses_future_covariates(self):
+    def uses_future_covariates(self) -> bool:
         """
         Whether the model uses future covariates, once fitted.
         """
         return self._uses_future_covariates
 
     @property
-    def uses_static_covariates(self):
+    def uses_static_covariates(self) -> bool:
         """
         Whether the model uses static covariates, once fitted.
         """
         return self._uses_static_covariates
 
     @property
-    def considers_static_covariates(self):
+    def considers_static_covariates(self) -> bool:
         """
         Whether the model considers static covariates, if there are any.
         """

darts/models/forecasting/kalman_forecaster.py

@@ -135,5 +135,9 @@ def _predict(
 
         return filtered_series[-n:]
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def _is_probabilistic(self) -> bool:
         return True

darts/models/forecasting/nbeats.py

@@ -755,6 +755,10 @@ def __init__(
         if isinstance(layer_widths, int):
             self.layer_widths = [layer_widths] * self.num_stacks
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module:
         # samples are made of (past_target, past_covariates, future_target)
         input_dim = train_sample[0].shape[1] + (

darts/models/forecasting/nhits.py

@@ -698,6 +698,10 @@ def __init__(
         if isinstance(layer_widths, int):
             self.layer_widths = [layer_widths] * self.num_stacks
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @staticmethod
     def _prepare_pooling_downsampling(
         pooling_kernel_sizes, n_freq_downsample, in_len, out_len, num_blocks, num_stacks

darts/models/forecasting/nlinear.py

@@ -407,6 +407,10 @@ def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module:
             **self.pl_module_params,
         )
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def supports_static_covariates(self) -> bool:
         return True

darts/models/forecasting/prophet_model.py

@@ -267,6 +267,10 @@ def _generate_predict_df(
             )
         return predict_df
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _is_probabilistic(self) -> bool:
         return True
 

darts/models/forecasting/regression_ensemble_model.py

@@ -99,7 +99,7 @@ def __init__(
             f"{regression_model.lags}",
         )
 
-        self.regression_model = regression_model
+        self.regression_model: RegressionModel = regression_model
         self.train_n_points = regression_train_n_points
 
     def _split_multi_ts_sequence(
@@ -115,7 +115,6 @@ def fit(
         past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
         future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
     ):
-
         super().fit(
             series, past_covariates=past_covariates, future_covariates=future_covariates
         )
@@ -189,7 +188,6 @@ def ensemble(
         series: Optional[Sequence[TimeSeries]] = None,
         num_samples: int = 1,
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
-
         is_single_series = isinstance(series, TimeSeries) or series is None
         predictions = series2seq(predictions)
         series = series2seq(series) if series is not None else [None]
@@ -219,6 +217,13 @@ def extreme_lags(
         extreme_lags_ = super().extreme_lags
         return (extreme_lags_[0] - self.train_n_points,) + extreme_lags_[1:]
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return (
+            super().supports_multivariate
+            and self.regression_model.supports_multivariate
+        )
+
     def _is_probabilistic(self) -> bool:
         """
         A RegressionEnsembleModel is probabilistic if its regression

darts/models/forecasting/regression_model.py

@@ -314,6 +314,14 @@ def extreme_lags(
             max_future_cov_lag,
         )
 
+    @property
+    def supports_multivariate(self) -> bool:
+        """
+        If available, uses `model`'s native multivariate support. If not available, obtains multivariate support by
+        wrapping the univariate model in a `sklearn.multioutput.MultiOutputRegressor`.
+        """
+        return True
+
     @property
     def min_train_series_length(self) -> int:
         return max(

darts/models/forecasting/rnn_model.py

@@ -470,6 +470,10 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset):
             "RNNModel requires a shifted training dataset with shift=1.",
         )
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def min_train_series_length(self) -> int:
         return self.training_length + 1

darts/models/forecasting/sf_auto_arima.py

@@ -106,6 +106,10 @@ def _predict(
 
         return self._build_forecast_series(samples)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     @property
     def min_train_series_length(self) -> int:
         return 10

darts/models/forecasting/sf_auto_ces.py

@@ -66,6 +66,10 @@ def predict(
 
         return self._build_forecast_series(mu)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     @property
     def min_train_series_length(self) -> int:
         return 10

darts/models/forecasting/sf_auto_ets.py

@@ -142,6 +142,10 @@ def _predict(
             samples = mu
         return self._build_forecast_series(samples)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     @property
     def min_train_series_length(self) -> int:
         return 10

darts/models/forecasting/sf_auto_theta.py

@@ -79,6 +79,10 @@ def predict(
 
         return self._build_forecast_series(samples)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     @property
     def min_train_series_length(self) -> int:
         return 10

darts/models/forecasting/tbats_model.py

@@ -221,6 +221,10 @@ def predict(self, n, num_samples=1, verbose: bool = False):
 
         return self._build_forecast_series(samples)
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return False
+
     def _is_probabilistic(self) -> bool:
         return True
 

darts/models/forecasting/tcn_model.py

@@ -449,6 +449,10 @@ def __init__(
         self.dropout = dropout
         self.weight_norm = weight_norm
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module:
         # samples are made of (past_target, past_covariates, future_target)
         input_dim = train_sample[0].shape[1] + (

darts/models/forecasting/tft_model.py

@@ -1140,6 +1140,10 @@ def _build_inference_dataset(
             use_static_covariates=self.uses_static_covariates,
         )
 
+    @property
+    def supports_multivariate(self) -> bool:
+        return True
+
     @property
     def supports_static_covariates(self) -> bool:
         return True