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default.yaml
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split: "val" # which dataset split to evaluate on. One of [test, val]
kwargs: # keyword arguments passed to the backtest eval runner. See backtest documentation
verbose: True
#retrain: True # This argument is already set to False for Global models and True for Locals (which require True). Use this argument here to override.
metric:
- _target_: darts.metrics.metrics.mse
_partial_: True
# you can add additional metrics
#- _target_: darts.metrics.metrics.r2_score
# _partial_: True
forecast_horizon: 24
stride: 24
# Note that setting start here will overwrite the values set by the model, which could introduce bugs.
# i.e. the start = None logic in darts does not work for all models currently, and start has therefore manually been set in each afflicted model config
#start: null
retrain: False
measure_execution_time: True
mc_dropout: False
log_metrics: True
metrics_per_series: True # if the split dataset consists of multiple series, setting this argument to True will produce metrics per series on the form {metric_name}_{series_idx}
plot: # use to control plotting of predictions. If omitted or plot: null, no plotting is performed.
every_n_prediction: 1 # with forecast_horizon > stride predictions will be overlapping. This argument controls how many predictions to plot
title: null
title_add_metrics: True
#presenter: # how the plot should be presented. Default is ["savefig", None]
# - "savefig" # will save figure to cfg.paths.output_dir / predictions / split_predictions
# - null # will return figure to caller
kwargs:
plot_covariates: False
plot_encodings: False
plot_weights: True
predictions:
save:
False # will save predictions to paths.output_dir/predictions/predictions.pkl
#data: True # also save the data that was predicted on to paths.output_dir/predictions/data.pkl
return: # will return the predictions from the src.eval.run function.
data: False # also return the data that was predicted on
ensemble_weights:
save: True