When Text Helps Time Series (and When It Doesn’t)
NeutralArtificial Intelligence
- The introduction of Time Series Foundation Models (TSFMs) marks a notable development in forecasting, yet their current architectural design restricts their effectiveness by analyzing time series independently. This oversight neglects the rich contextual information that could improve forecasting accuracy.
- The implications of this limitation are significant, as it suggests that for TSFMs to reach their full potential, a shift towards a more holistic approach is necessary. This could enhance their application across various fields, ultimately leading to more accurate predictions and better decision
— via World Pulse Now AI Editorial System