ProFIT: Prototype-Guided Latent Diffusion for Few-Shot Irregular Time Series Forecasting

Reliable forecasts are often required for a newly deployed weather station, a newly monitored patient, or a new wearable user before sufffcient task-speciffc history can be collected. This setting is particularly challenging when observations are sparse and asynchronous: an unseen task may differ from the training tasks in both its underlying dynamics and its sampling mechanism, whereas existing irregular forecasters typically require substantial within-task history and most few-shot models assume regularly sampled inputs. We propose ProFIT, a Prototype-Guided Latent Diffusion framework that infers a continuous-time task function from only a few observed events. ProFIT ffrst encodes complete meta-training tasks directly as sets of timestamped variable-value events, learning a global function latent space without interpolation or regulargrid assumptions. For a new task, a sampling-aware matcher combines observed values with sampling statistics to retrieve representative task prototypes, which guide conditional latent diffusion over plausible function latents. A forecast-alignment objective further constrains generated latents to preserve the predictive semantics of complete-task representations. The resulting latent is decoded at arbitrary query times to produce probabilistic forecasts. Experiments on multiple realworld irregular time-series datasets show that ProFIT consistently achieves strong forecasting performance. These results highlight the potential of prototype-guided latent diffusion for forecasting unseen irregular processes from extremely limited observations.