Language Models Still Struggle to Zero-shot Reason about Time Series

Understand

Time series are critical for decision-making in fields like finance and healthcare.

• Their importance has driven a recent influx of works passing time series into language models, leading to non-trivial forecasting on some datasets.
• But it remains unknown whether non-trivial forecasting implies that language models can reason about time series.
• To address this gap, we generate a first-of-its-kind evaluation framework for time series reasoning, including formal tasks and a corresponding dataset of multi-scale time series paired with text captions across ten domains.