Information Anchors: Understanding and Using Forecast-Relevant Information in Time-Series Foundation Models

Time-series foundation models (TSFMs) achieve strong forecasting performance, but output accuracy alone reveals little about what internal information their forecasts rely on, how that information is organized, or whether it is stable and functionally meaningful. We introduce INFORMATION ANCHORS, a framework for identifying hidden representations that carry strong information about the future. Using mutual information as a common measure, our framework reveals where forecast-relevant information concentrates, what forecast properties it represents, and how strongly model predictions respond to it. Across six heterogeneous TSFMs and seven data regimes, we find that forecast-relevant information is concentrated in a limited set of temporal regions rather than uniformly distributed across model representations. Compared with representations carrying less future information, information anchors expose more forecast properties, are associated with distinctive patterns in the observed history, and produce stronger prediction responses under controlled intervention. We further demonstrate the practical value of this information through ANCHOR-RAG, which uses information-anchor signals to guide the retrieval and integration of historical evidence. Overall, INFORMATION ANCHORS makes forecast-relevant information in TSFMs identifiable, interpretable, and practically useful, providing a general framework that connects model understanding with improved forecasting.