Multi-Timeframe Drummond Patches and JEPA Pre-Training for Short-Term Retail OHLC Series Forecasting
https://doi.org/10.26907/1562-5419-2026-29-1-351-367
Abstract
We propose a method for constructing scale-invariant representations of retail revenue time series based on three-bar Drummond Geometry (DG) computed over three adjacent periods, extended with a multi-timeframe context (day, partial calendar week, and a rolling 7-day window). Self-supervised pre-training on these “patches” is performed using a Joint-Embedding Predictive Architecture (JEPA) with spatiotemporal masking, followed by fine-tuning with output heads that quantify predictive uncertainty for next-day and next-week forecasts. The work analyzes the properties of affine invariance of the features and the identifiability of the weekly phase; empirical improvement over strong baseline models on real-world data is demonstrated.
About the Authors
Alexander Semenovich Sizov
Southwest State University
Russian Federation
Russian Federation
Yuri Alekseevich Khalin
Southwest State University
Russian Federation
Russian Federation
Artem Aleksandrovich Belykh
Southwest State University
Russian Federation
Russian Federation
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Review
For citations:
Sizov A.S., Khalin Yu.A., Belykh A.A. Multi-Timeframe Drummond Patches and JEPA Pre-Training for Short-Term Retail OHLC Series Forecasting. Russian Digital Libraries Journal. 2026;29(1):351-367. (In Russ.) https://doi.org/10.26907/1562-5419-2026-29-1-351-367
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