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The vast majority of time-series forecasting approaches require a substantialtraining dataset. However, many real-life forecasting applications have verylittle initial observations, sometimes just 40 or fewer. Thus, theapplicability of most forecasting methods is restricted in data-sparsecommercial applications. While there is recent work in the setting of verylimited initial data (so-called `zero-shot' forecasting), its performance isinconsistent depending on the data used for pretraining. In this work, we takea different approach and devise ForecastPFN, the first zero-shot forecastingmodel trained purely on a novel synthetic data distribution. ForecastPFN is aprior-data fitted network, trained to approximate Bayesian inference, which canmake predictions on a new time series dataset in a single forward pass. Throughextensive experiments, we show that zero-shot predictions made by ForecastPFNare more accurate and faster compared to state-of-the-art forecasting methods,even when the other methods are allowed to train on hundreds of additionalin-distribution data points.

ForecastPFN: Synthetically-Trained Zero-Shot Forecasting