Training-Free Conformal Interval Emerges as Strong Baseline for Probabilistic Time-Series Forecasting
Researchers demonstrate that a simple, parameter-free conformal interval baseline—using only the last observed value wrapped in split-conformal residual quantiles—substantially outperforms many learned forecasting methods across 2,217 real time series. The method, called ConformalNaive, beats established baselines like NPTS and Conformal Seasonal Pools on one-step-ahead forecasting while maintaining better calibration than trained neural forecasters. The finding argues for mandatory inclusion of this trivial baseline in future probabilistic forecasting comparisons to avoid overstating the gains of more complex learned methods.
A new arXiv preprint challenges the evaluation practices in probabilistic time-series forecasting by showing that an extremely simple, training-free baseline—a conformal interval built from the last observed value and finite-sample split-conformal residual quantiles—is far stronger than its near-absence from recent literature suggests. Testing across 2,217 real series from nine public datasets, ConformalNaive decisively outperforms naive value-quantile baselines and the NPTS family (beating 64–73% of series), and matches simpler learned conformal methods like quantile regression. It is only surpassed by adaptive-online and ensemble methods that explicitly track distribution shift. Notably, on six datasets that introduced DeepNPTS, the trivial conformal floor achieves 84–85% coverage at nominal 95% confidence, compared to DeepNPTS's 66%, indicating better calibration. The authors also introduce ConformalNaive+, a one-line horizon-adaptive selector that restores coverage by choosing between complementary floors. The work argues that this mandatory baseline must be included in all future claims of learned forecaster improvements.
What's missing
The preprint does not discuss computational cost or scalability comparisons between ConformalNaive and the learned methods it benchmarks against. Additionally, while the paper maps the boundary where seasonal methods outperform the random-walk floor at multi-step horizons, the mechanisms driving this transition and guidance for practitioners on method selection across different forecasting scenarios could be more explicit.
What different sources said
- arXiv cs.LGCenter
Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
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