Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
Braverman Readings in Machine Learning
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A training-free conformal naive interval based on last-value forecasts provides stronger or comparable performance to many learned methods in one-step probabilistic time series forecasting and should be required as a baseline.
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Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools
Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
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Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
A training-free conformal naive interval based on last-value forecasts provides stronger or comparable performance to many learned methods in one-step probabilistic time series forecasting and should be required as a baseline.