Multi-Timeframe Drummond Patches and JEPA Pre-Training for Short-Term Retail OHLC Series Forecasting
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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.
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References
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