Rethinking Multimodal Time-Series Forecasting Evaluation
TL;DR
A thorough empirical study of zero-shot multimodal forecasting approaches on TimesX suggests that many approaches that perform well on existing benchmarks may fail on TimesX, and simple ensemble methods that leverage rich textual context accompanying time-series can outperform strong baselines on TimesX.
Abstract
We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world time series with diverse domains and textual contexts obtained from an automated data generation pipeline, which helps address three main issues of existing multimodal forecasting benchmarks: (1) poor generalization due to the small scale and synthetic nature of benchmark data, (2) very limited types of textual contexts in the benchmarks, and (3) an inability to mitigate data leakage in evaluation. We conduct a thorough empirical study of zero-shot multimodal forecasting approaches on TimesX. Our results suggest that many approaches that perform well on existing benchmarks may fail on TimesX. In contrast, simple ensemble methods that leverage rich textual context accompanying time-series can outperform strong baselines on TimesX.