FOMO: Forecasting using Other Markets Observations

Time-series foundation models (TSFMs) increasingly accept multivariate inputs, yet adding related series as context can make their forecasts worse, a problem we call context degradation. In financial markets where contracts often have several related ones, this effect can be severe and unpredictable. The same kind of context that degrades one model by up to 15.6% can improve another by 6.8%. We find that the outcome depends on where series interact inside the network, and that early cross-series attention moves the target’s representation away from the state it has without context. We introduce FOMO (Forecasting with Other Markets’ Observations), a training-free algorithm that supplies related market histories as context and restricts cross-series attention to the later layers of a TSFM. Our experiments on four TSFMs and five datasets spanning prediction markets, equities, and crypto show that FOMO never falls below target-only forecasting in any setting where it changes the model, whereas native context mixing significantly degrades forecasts in half of the held-out settings. FOMO also reduces the worst-decile loss in every held-out setting, by a median of 5.3×.