Abstract:We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input
to any forecasting model. TopoPrimer improves accuracy across diverse domains, stabilizes forecasts under seasonal demand
spikes, and closes the cold-start gap. Precomputed once per domain via persistent homology and spectral sheaf coordinates,
TopoPrimer deploys per token for fully-trained models and as a lightweight adapter for pre-trained backbones. Of these two
components, sheaf coordinates are the primary accuracy driver. Across four public benchmarks on Chronos and TimesFM,
TopoPrimer consistently improves forecasting accuracy, with gains of up to 7.3% MSE on ECL. The topology advantage persists
with near-identical magnitude across zero-shot and fine-tuned backbones, suggesting topology and per-series training
capture complementary signals. The gains are most pronounced in difficult regimes. Under peak seasonal demand, classical
and zero-shot models degrade by up to 50%, while TopoPrimer stays within 10%. At cold start with no item history,
TopoPrimer reduces MAE by 27% over a topology-free baseline.
| Comments: | 29 pages, 16 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.15035 [cs.LG] |
| (or arXiv:2605.15035v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15035 arXiv-issued DOI via DataCite (pending registration) |
From: Zara Zetlin [view email]
[v1]
Thu, 14 May 2026 16:30:25 UTC (28,759 KB)
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