FIX-2633: Use correct time indices when running historical forecasts …

…on regression models with 'output_chunk_shift > 0' and 'output_chunk_length == 1'. Extended unit tests to cover this

darts/tests/utils/historical_forecasts/test_historical_forecasts.py

@@ -59,7 +59,20 @@
     from darts.utils.likelihood_models import GaussianLikelihood, QuantileRegression
 
 models = [LinearRegressionModel, NaiveDrift]
-models_reg_no_cov_cls_kwargs = [(LinearRegressionModel, {"lags": 8}, {}, (8, 1))]
+models_reg_no_cov_cls_kwargs = [
+    (LinearRegressionModel, {"lags": 8}, {}, (8, 1)),
+    # output_chunk_length only
+    (LinearRegressionModel, {"lags": 5, "output_chunk_length": 2}, {}, (5, 1)),
+    # output_chunk_shift only
+    (LinearRegressionModel, {"lags": 5, "output_chunk_shift": 1}, {}, (5, 1)),
+    # output_chunk_shift + output_chunk_length only
+    (
+        LinearRegressionModel,
+        {"lags": 5, "output_chunk_shift": 1, "output_chunk_length": 2},
+        {},
+        (5, 1),
+    ),
+]
 if not isinstance(CatBoostModel, NotImportedModule):
     models_reg_no_cov_cls_kwargs.append((
         CatBoostModel,
@@ -656,6 +669,22 @@ def test_historical_forecasts(self, config):
         model_cls, kwargs, model_kwarg, bounds = config
         model = model_cls(**kwargs, **model_kwarg)
 
+        if model.output_chunk_shift > 0:
+            with pytest.raises(ValueError) as err:
+                forecasts = model.historical_forecasts(
+                    series=self.ts_pass_val,
+                    forecast_horizon=forecast_horizon,
+                    stride=1,
+                    train_length=train_length,
+                    retrain=True,
+                    overlap_end=False,
+                )
+            assert str(err.value).startswith(
+                "Cannot perform auto-regression `(n > output_chunk_length)`"
+            )
+            # continue the test without autogregression if we are using shifts
+            forecast_horizon = model.output_chunk_length
+
         # time index
         forecasts = model.historical_forecasts(
             series=self.ts_pass_val,
@@ -1153,6 +1182,22 @@ def test_regression_auto_start_multiple_no_cov(self, config):
         )
         model.fit(self.ts_pass_train)
 
+        if model.output_chunk_shift > 0:
+            with pytest.raises(ValueError) as err:
+                forecasts = model.historical_forecasts(
+                    series=[self.ts_pass_val, self.ts_pass_val],
+                    forecast_horizon=forecast_horizon,
+                    train_length=train_length,
+                    stride=1,
+                    retrain=True,
+                    overlap_end=False,
+                )
+            assert str(err.value).startswith(
+                "Cannot perform auto-regression `(n > output_chunk_length)`"
+            )
+            # continue the test without autogregression if we are using shifts
+            forecast_horizon = model.output_chunk_length
+
         forecasts = model.historical_forecasts(
             series=[self.ts_pass_val, self.ts_pass_val],
             forecast_horizon=forecast_horizon,

darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py

@@ -162,19 +162,24 @@ def _optimized_historical_forecasts_last_points_only(
         else:
             forecast = forecast[:, 0]
 
+        if (
+            stride == 1
+            and model.output_chunk_length == 1
+            and model.output_chunk_shift == 0
+        ):
+            times = times[0]
+        else:
+            times = generate_index(
+                start=hist_fct_start
+                + (forecast_horizon + model.output_chunk_shift - 1) * freq,
+                length=forecast.shape[0],
+                freq=freq * stride,
+                name=series_.time_index.name,
+            )
+
         forecasts_list.append(
             TimeSeries.from_times_and_values(
-                times=(
-                    times[0]
-                    if stride == 1 and model.output_chunk_length == 1
-                    else generate_index(
-                        start=hist_fct_start
-                        + (forecast_horizon + model.output_chunk_shift - 1) * freq,
-                        length=forecast.shape[0],
-                        freq=freq * stride,
-                        name=series_.time_index.name,
-                    )
-                ),
+                times=times,
                 values=forecast,
                 columns=forecast_components,
                 static_covariates=series_.static_covariates,