  The vast majority of time-series forecasting approaches require a substantial
training dataset. However, many real-life forecasting applications have very
little initial observations, sometimes just 40 or fewer. Thus, the
applicability of most forecasting methods is restricted in data-sparse
commercial applications. While there is recent work in the setting of very
limited initial data (so-called `zero-shot' forecasting), its performance is
inconsistent depending on the data used for pretraining. In this work, we take
a different approach and devise ForecastPFN, the first zero-shot forecasting
model trained purely on a novel synthetic data distribution. ForecastPFN is a
prior-data fitted network, trained to approximate Bayesian inference, which can
make predictions on a new time series dataset in a single forward pass. Through
extensive experiments, we show that zero-shot predictions made by ForecastPFN
are more accurate and faster compared to state-of-the-art forecasting methods,
even when the other methods are allowed to train on hundreds of additional
in-distribution data points.
