Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift

📝 Original Info

• Title: Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
• ArXiv ID: 2511.05619
• Date: 2025-11-06
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. 실제 논문을 확인하여 저자명을 입력하시기 바랍니다. **

📝 Abstract

Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the dominant frequency components in downstream tasks and those represented during pretraining) as a key factor. We present evidence from an industrial-scale player engagement prediction task in mobile gaming, where TSFMs underperform domain-adapted baselines. To isolate the mechanism, we design controlled synthetic experiments contrasting signals with seen versus unseen frequency bands, observing systematic degradation under spectral mismatch. These findings position frequency awareness as critical for robust TSFM deployment and motivate new pretraining and evaluation protocols that explicitly account for spectral diversity.

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