Investigating Hallucinations in Time Series Foundation Models through Signal Subspace Analysis
Yufeng Zou, Zijian Wang, Diego Klabjan, Han Liu
Times series foundation models (TSFMs) have emerged as a promising paradigm for time series analysis, showing remarkable generalization performance across different domains. While there has been research on hallucinations in foundation models, hallucinations in TSFMs are underexplored. In this paper, we formally define TSFM hallucinations in the zero-shot forecasting setting by examining whether a generated forecast exhibits different dynamics from those of the context. Our study reveals that TSFM hallucinations are mainly caused by the loss of context information in hidden states during forward propagation. As such, we propose methodologies to identify signal subspaces in TSFMs and magnify the signal subspace information through intervention. Extensive experiments demonstrate that our proposed intervention approach effectively mitigates hallucinations and improves forecast performance. Furthermore, the signal strength measure we compute from signal subspaces has strong predictive power of hallucinations and forecast performance of the model. Our work contributes to deeper understanding of TSFM trustworthiness that could foster future research in this direction.