GIFT-Eval: A Benchmark for General Time Series Forecasting Model Evaluation

Abstract

The development of time series foundation models has been constrained by the absence of comprehensive benchmarks. This paper introduces the **G**eneral T**I**me Series **F**orecas**T**ing Model **Eval**uation, GIFT-Eval, a pioneering benchmark specifically designed to address this gap. GIFT-Eval encompasses 28 datasets with over 144,000 time series and 157 million observations, spanning seven domains and featuring a variety of frequencies, number of variates and prediction lengths from short to long-term forecasts. Our benchmark facilitates the effective pretraining and evaluation of foundation models. We present a detailed analysis of 12 baseline models, including statistical, deep learning, and foundation models. We further provide a fine-grained analysis for each model across different characteristics of our benchmark. We hope that insights gleaned from this analysis along with the access to this new standard zero-shot time series forecasting benchmark shall guide future developments in time series forecasting foundation models.