Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.15035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909043991248896 |
|---|---|
| author | Zetlin, Zara Moharreri, Kayhan Safi, Maria |
| author_facet | Zetlin, Zara Moharreri, Kayhan Safi, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TopoPrimer: The Missing Topological Context in Forecasting Models Zetlin, Zara Moharreri, Kayhan Safi, Maria Machine Learning 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. |
| title | TopoPrimer: The Missing Topological Context in Forecasting Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.15035 |