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Research questionHow can interval-based time-series classifiers speed up frequent inference without materially reducing accuracy?Extracting exact quantiles across intervals can require costly sorting when a classifier processes many time series. Reducing this computation may improve inference speed while introducing some predictive-performance loss.
Inference Optimization
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.MomentQuant: an even more minimalist interval method with linear time complexity for time series classificationThe source concerns Quant, an interval-based time-series classifier using recursive fixed dyadic intervals and quantile features. It reports comparisons between an optimized exact implementation and MomentQuant, which uses Cornish–Fisher approximate quantiles to avoid sorting; the evidence covers faster inference with a small reported decrease in predictive performance, especially when inference is performed more often than training.research paper · Sep 4, 2026
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