AI BriefingTopoPrimer: Supplementing the Topological Context Missing from Time-Series Forecasting Models
Key point
This introduces the TopoPrimer framework, which improves forecasting accuracy by leveraging the global topological structure of time-series data as an input.
Details
We propose TopoPrimer, a framework that converts the global topological structure of time-series data into an explicit input for forecasting models. This framework improves forecasting accuracy across various domains, maintains stable forecasts even during seasonal demand surges, and addresses the Cold-start problem where data is scarce.
TopoPrimer only needs to be precomputed once per domain via Persistent Homology and Spectral Sheaf Coordinates. There are two ways to apply the model:
- Fully-trained models: Applied by placing TopoPrimer at each Token
- Pre-trained backbones: Applied in the form of a lightweight Adapter
In particular, Sheaf Coordinates serve as a key factor in improving forecasting accuracy, and its performance was demonstrated on 4 public benchmarks, including Chronos.
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