Time Series Foundation Models Can Be Effective Forecasters for Irregular Time Series

Time series foundation models (TSFMs) offer strong zero-shot forecasting across domains. Their ability to transfer without task-specific training makes them attractive for sparse irregular forecasting, yet they are typically applied to regularly sampled sequences. Inspired by temporal point process modeling, we study an inference-time event-sequence interface that represents each observed event by its value and inter-event interval, enabling frozen TSFMs to produce next-event value and timing forecasts without regular-grid imputation. On 85 Time-IMM series from nine dataset families, the event-sequence interface achieves lower normalized value MAE and MSE than regular-grid interfaces for each of the five evaluated TSFMs; both errors are also lower in 8 of 9 dataset families. Both interfaces use the same frozen TSFM checkpoint and are evaluated on the same held-out examples, providing evidence that the input interface can be an important performance bottleneck for frozen TSFMs on irregular time series. Compared with four trained irregular baselines, frozen TSFMs using the event-sequence interface achieve lower next-event value and timing errors. On seven EasyTPP datasets, frozen TSFMs using the event-sequence interface provide competitive next-event timing forecasts relative to TPP baselines trained on each dataset. Together, the results show that frozen TSFMs can effectively forecast sparse irregular time series through the event-sequence interface.