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enhancementAdding new functionalityAdding new functionalitymodule:forecastingforecasting module: forecasting, incl probabilistic and hierarchical forecastingforecasting module: forecasting, incl probabilistic and hierarchical forecasting
Description
The use of a range for the fh argument of the splitter is problematic and not transparent to the user as it allows the range to begin at 0 without any warning. Starting at 0 will include the last point of the training set in the test. It is very easy for the user to forget to explicitly declare 1 as the beginning of the range.
Example:
benchmark2 = ForecastingBenchmark()
scorers = [MeanSquaredError(square_root=True)]
single_fold_splitter = SingleWindowSplitter(window_length = 52, fh=range(1,5))
# Starting at 0 would include the last value of the in-sample data (training set) in the test set (!)
benchmark2.add_task(
y_sample_sorted,
single_fold_splitter,
scorers
)
benchmark2.add_estimator(
ChronosForecaster("amazon/chronos-bolt-tiny"), estimator_id="chronos_bolt"
)
benchmark2.add_estimator(
ChronosForecaster("amazon/chronos-bolt-base"), estimator_id="chronos_base"
)
benchmark2.add_estimator(
TinyTimeMixerForecaster()
)
benchmark2.add_estimator(
MOIRAIForecaster(checkpoint_path=f"sktime/moirai-1.0-R-small"), estimator_id="moirai_small"
)
benchmark2.add_estimator(
MOIRAIForecaster(checkpoint_path=f"sktime/moirai-1.0-R-large"), estimator_id="moirai_large"
)
results2_df = benchmark2.run(local_output_path2)The proposed enhancement is to display a warning as a minimum, or implement a hard restriction so that starting the range at 0 is not allowed to enforeced best practices in cross-validation.
This issue is related to:
#4716
sktime/tutorial_haicon_prologue_day#1
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enhancementAdding new functionalityAdding new functionalitymodule:forecastingforecasting module: forecasting, incl probabilistic and hierarchical forecastingforecasting module: forecasting, incl probabilistic and hierarchical forecasting