Spectral Predictability as a Fast Reliability Indicator for Time Series Forecasting Model Selection

The recent study on spectral predictability (Ω) presents a significant advancement in time series forecasting model selection. As practitioners often face the challenge of validating numerous models, the introduction of Ω offers a swift solution, taking mere seconds to compute per dataset. The research analyzed 51 models across 28 datasets from the GIFT-Eval benchmark, revealing that large time series foundation models (TSFMs) consistently outperform lightweight task-trained baselines when Ω is high. This finding is crucial as it not only streamlines the model selection process but also highlights the importance of focusing on models that can tackle genuinely difficult problems, particularly those with low Ω values. By providing a practical first-pass filter, this approach reduces validation costs and aids practitioners in making informed decisions about their forecasting strategies.