Scenario Forecasting from Time Series via Mechanism Inference

Time-series forecasting supports timely decisions in public health, macroeconomics, and online markets. However, the same observed history can be consistent with different evolution rules, which may imply different future outcomes and preparation needs. A distribution over future trajectories alone does not make these alternatives explicit or show how predictions depend on the underlying dynamics. We introduce mechanism-informed scenario forecasting, which groups plausible evolution mechanisms into scenarios and predicts their probabilities and conditional future distributions. Both the scenario definitions and their number depend on the observed history. Since real histories typically lack mechanism labels, we train our proposed method, TimeStone, on synthetic histories paired with their generating mechanisms. Following the prior-data fitted network approach, the model learns to infer a distribution over mechanisms from a new history. It simulates futures under sampled mechanisms and groups these mechanisms into scenarios. Experiments on three real-world datasets show strong scenario quality and competitive performance in trajectory forecasting and event prediction.