ForecastPFN: Synthetically-Trained Zero-Shot Forecasting

• URL: http://arxiv.org/abs/2311.01933v1
• Date: Fri, 3 Nov 2023 14:17:11 GMT
• Title: ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
• Authors: Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha Naidu, Colin White
• Abstract summary: ForecastPFN is the first zero-shot forecasting model trained purely on a novel synthetic data distribution. We show that zero-shot predictions made by ForecastPFN are more accurate and faster compared to state-of-the-art forecasting methods.
• Score: 16.12148632541671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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