Time-${\pi }_0$: A Time Series Action Model for Decision-Making

Time series foundation models (TSFMs) have recently demonstrated a strong capability in modeling temporal dynamics, yet their potential for real-world operational decision-making remains largely unexplored. In this work, we formulate time series decision-making as the task of generating actions based on historical observations of an exogenous time series and the current state of a system. We introduce Time-${\pi }_0$, a simple yet effective framework that adapts pretrained TSFMs for direct action generation. Specifically, a TSFM encodes temporal history into context tokens, while a lightweight flow-matching action expert combines these representations with the system state to generate discrete action chunks. We further construct $\text{Cassandra}$, a testbed comprising five real-world operational systems across energy, finance, telecommunication, supply chains and green industries. Each system couples an action-independent exogenous time series with a controllable system state. Experiments under single-chunk and rollout evaluations show that Time-${\pi }_0$ achieves strong performance compared with forecast-then-optimize and direct offline policy baselines. Overall, our findings lay the groundwork for expanding the utility of TSFMs, shifting their paradigm from standard forecasting to actionable, real-world decision-making. The code and testbed are available.