feature/multivariate - step 4 #111
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…ade rnn and tcn model to inherit from it
…caused by merging
… frequency and inferred frequency don't match, but just a warning
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| # TODO parameterize the moving window | |||
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| def _create_parameter_dicts(model, target_indices, component_index, use_full_output_length): | |||
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I believe this is what @guillaumeraille was asking on the daily in the morning.
@pennfranc this looks good, think we can also somehow make it automatic?
| def backtest_forecasting(series: TimeSeries, | ||
| model: ForecastingModel, | ||
| start: pd.Timestamp, | ||
| fcast_horizon_n: int, | ||
| target_indices: Optional[List[int]] = None, |
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Quite a lot of args passed to this function. As an optional improvement can consider moving them to a class level and wrap the whole backtesting module in a class.
| use_full_output_length | ||
| In case `model` is a subclass of `TorchForecastingModel`, this argument will be passed along | ||
| as argument to the predict method of `model`. | ||
| stride |
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very nice. It would also be nice to have the option of configuring the training set length. When set, this would do moving window, and when not set it would do expending window (can wait a future PR though :)
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Yes for sure, @guillaumeraille also suggested this!
darts/backtesting/backtesting.py
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| stride | ||
| The number of time steps (the unit being the frequency of `series`) between two consecutive predictions. | ||
| retrain | ||
| Whether to retrain the model for every prediction or not. Currently only TorchForecastingModel |
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You can add backticks around TorchForecastingModel: `
darts/backtesting/backtesting.py
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| last_pred_time = ( | ||
| series.time_index()[-fcast_horizon_n - stride] if trim_to_series else series.time_index()[-stride - 1] | ||
| ) | ||
| raise_if_not(retrain or isinstance(model, TorchForecastingModel), "Only 'TorchForecastingModel' instances" |
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you can move this check up (before computing last_pred_time
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