Examples/m4 competition #138
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…les/M4competion
…les/M4competion
…les/M4competion
…les/M4competion
darts/models/theta.py
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| @@ -95,7 +95,7 @@ def fit(self, series: TimeSeries, component_index: Optional[int] = None): | |||
| new_ts = remove_from_series(ts, self.seasonality, model=self.season_mode) | |||
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| # SES part of the decomposition. | |||
| self.model = hw.SimpleExpSmoothing(new_ts.values()).fit() | |||
| self.model = hw.SimpleExpSmoothing(new_ts.values()).fit(initial_level=0.2) | |||
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Do we want to change this in this PR?
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Sorry, it was something that I forgot to clean.
Same for the comments after that
| holt = ExponentialSmoothing(seasonal=None, damped=False, trend='additive', seasonal_periods=m) | ||
| damp = ExponentialSmoothing(seasonal=None, damped=True, trend='additive', seasonal_periods=m) | ||
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| # season_mode = ["additive", "multiplicative"] |
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Should this be here if it's commented out?
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| def train_4theta(ts, n): | ||
| # season_mode = ["additive", "multiplicative"] |
examples/M4_competition/FourTheta.py
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| return model_class(**best_param_combination) | ||
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| class FourTheta(UnivariateForecastingModel): |
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This class is already included in the previous PR and merged, right? Or is it some different implementation?
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The difference is that it uses a modified version of gridsearch that do not create a TimeSeries.
Thus the execution time of the notebook drops from 1h to 5-10 minutes
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I know it is a pity that using TimeSeries increases the gridsearch time, but if we release this we cannot have a separate implementation, and we should really use the implementation in darts/models, even if it takes much longer to run.
…les/M4competion
M4 competition Benchmark
Summary
Proposition for adding M4 competition benchmark to examples. Add scripts to download dataset, create the timeseries and reproduce the results. Add a notebook to put together the different parts and and visualize some results.