OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting

Understand

Time series forecasting is fundamental to diverse applications, with recent approaches leverage large vision models (LVMs) to capture temporal patterns through visual representations.

• We reveal that while vision models enhance forecasting performance, 99% of their parameters are unnecessary for time series tasks.
• Through cross-modal analysis, we find that time series align with low-level textural features but not high-level semantics, which can impair forecasting accuracy.
• We propose OccamVTS, a knowledge distillation framework that extracts only the essential 1% of predictive information from LVMs into lightweight networks.