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

Tianze Wang, Sofiane ENNADIR, John Pertoft, Gabriela Zarzar Gandler, Lele Cao, Zineb Senane, Styliani Katsarou, Sahar Asadi, Axel Karlsson, Oleg Smirnov

Keywords: Time Series Foundation Models, Frequency Shift, Domain Adaptation, Spectral Robustness, Gaming Industry, Player Engagement Prediction

TL;DR: We observe TSFMs lag domain-adapted baselines on a gaming engagement task; controlled synthetic experiments built from public datasets confirm sensitivity to frequency shifts, motivating frequency-aware pretraining and evaluation.

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.

Submission Number: 19