Fix/hfc retrain with tfm and ocs (#2869)

* update ocs

* try fixing ocs retrain start issue

* add ocs test for torch models

* update tests

Author: dennisbader

CHANGELOG.md

@@ -24,6 +24,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - Fixed a bug in `darts.utils.timeseries_generation.sine_timeseries()`, where the returned series ignored the specified `dtype`. [#2856](https://github.com/unit8co/darts/pull/2856) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed a bug in `TimeSeries.__getitem__()`, where indexing with a list of integers of `length <= 2` resulted in an error. [#2857](https://github.com/unit8co/darts/pull/2857) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed a bug in `TorchForecastingModel` which raised an error when calling any predict method after training the model with `fit_from_dataset()` on a dataset that uses static covariates. [#2860](https://github.com/unit8co/darts/pull/2860) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed a bug in `ForecastingModel.historical_forecasts`, where calling the method with `retrain=True`, `start=None`, and a model that uses `output_chunk_shift > 0` raised an error due to the start point not being properly inferred. [#2869](https://github.com/unit8co/darts/pull/2869) by [Dennis Bader](https://github.com/dennisbader).
 - Removed `darts/tests` and `examples` from the Darts package distribution. These are only required for internal testing. [#2854](https://github.com/unit8co/darts/pull/2854) by [Dennis Bader](https://github.com/dennisbader).
 
 **Dependencies**

darts/models/forecasting/forecasting_model.py

@@ -1111,7 +1111,7 @@ def retrain_func(
             )
 
             # adjust the start of the series depending on whether we train (at some point), or predict only
-            # must be performed after the operation on historical_foreacsts_time_index
+            # must be performed after the operation on historical_forecasts_time_index
             if min_timestamp_series > series_.time_index[0]:
                 series_ = series_.drop_before(min_timestamp_series - 1 * series_.freq)