feat: add quantiles support for missing models#336
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Pull request overview
Adds quantile-forecast support to previously non-probabilistic wrappers (AutoLGBM, AutoNHITS, AutoTFT) so they can return consistent model-q-{percentile} columns across the library.
Changes:
- Enable quantile outputs for NeuralForecast AutoNHITS/AutoTFT via
MQLossand post-processing throughQuantileConverter. - Enable quantile outputs for MLForecast AutoLGBM via conformal prediction intervals (
PredictionIntervals) and conversion to quantile columns. - Update model tests and add a v0.0.26 changelog entry describing the new quantile support and limitations.
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
timecopilot/models/neural.py |
Adds quantile support for AutoNHITS/AutoTFT by training with MQLoss when quantiles are requested and converting outputs to -q- columns. |
timecopilot/models/ml.py |
Adds quantile support for AutoLGBM using conformal prediction intervals and QuantileConverter to emit -q- columns. |
tests/models/test_models.py |
Updates quantile/level behavior expectations for AutoLGBM/AutoNHITS/AutoTFT and adjusts monotonicity assertions accordingly. |
docs/changelogs/v0.0.26.md |
Documents the new quantile support and usage example for the affected models. |
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this pr adds quantiles to missing models