time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models

• URL: http://arxiv.org/abs/2509.05801v2
• Date: Sat, 04 Oct 2025 15:13:16 GMT
• Title: time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
• Authors: Debdeep Sanyal, Aaryan Nagpal, Dhruv Kumar, Murari Mandal, Saurabh Deshpande,
• Abstract summary: We introduce transplantation activation, a causal intervention that manipulates hidden states by imposing the statistical moments of one event onto another.<n>We find that models encode a graded notion of event severity, with the latent vector norm directly correlating with the magnitude of systemic shocks.<n>Our findings provide evidence for a latent concept space that governs model predictions, shifting interpretability from post-hoc attribution to direct causal intervention.
• Score: 7.400285974510125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While transformer-based foundation models excel at forecasting routine patterns, two questions remain: do they internalize semantic concepts such as market regimes, or merely fit curves? And can their internal representations be leveraged to simulate rare, high-stakes events such as market crashes? To investigate this, we introduce activation transplantation, a causal intervention that manipulates hidden states by imposing the statistical moments of one event (e.g., a historical crash) onto another (e.g., a calm period) during the forward pass. This procedure deterministically steers forecasts: injecting crash semantics induces downturn predictions, while injecting calm semantics suppresses crashes and restores stability. Beyond binary control, we find that models encode a graded notion of event severity, with the latent vector norm directly correlating with the magnitude of systemic shocks. Validated across two architecturally distinct TSFMs, Toto (decoder only) and Chronos (encoder-decoder), our results demonstrate that steerable, semantically grounded representations are a robust property of large time series transformers. Our findings provide evidence for a latent concept space that governs model predictions, shifting interpretability from post-hoc attribution to direct causal intervention, and enabling semantic "what-if" analysis for strategic stress-testing.

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