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

TL;DR

This paper investigates why time series foundation models often fail under spectral shift, revealing that frequency mismatch between training and downstream tasks significantly impacts their generalization, especially in industrial applications.

Contribution

It identifies spectral shift as a key challenge for TSFMs and proposes the importance of frequency awareness in pretraining and evaluation protocols.

Findings

TSFMs underperform domain-adapted baselines in industrial tasks.

Spectral mismatch causes systematic degradation in synthetic experiments.

Frequency awareness is critical for robust TSFM deployment.

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.