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Metrics, performance, and subsequence detection
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@jdb78 jdb78 released this 28 Sep 19:58
v0.4.0
8c7277a
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Learn about vigilant mode.

Added

Models

  • Backcast loss for N-BEATS network for better regularisation
  • logging_metrics as explicit arguments to models

Metrics

  • MASE (Mean absolute scaled error) metric for training and reporting
  • Metrics can be composed, e.g. 0.3* metric1 + 0.7 * metric2
  • Aggregation metric that is computed on mean prediction over all samples to reduce mean-bias

Data

  • Increased speed of parsing data with missing datapoints. About 2s for 1M data points. If numba is installed, 0.2s for 1M data points
  • Time-synchronize samples in batches: ensure that all samples in each batch have with same time index in decoder

Breaking changes

  • Improved subsequence detection in TimeSeriesDataSet ensures that there exists a subsequence starting and ending on each point in time.
  • Fix min_encoder_length = 0 being ignored and processed as min_encoder_length = max_encoder_length

Contributors

  • jdb78
  • dehoyosb
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