Key Findings:

• Challenges with Reasoning: Language models exhibit only marginal abilities, performing slightly better than random in complex time reasoning tasks such as etiological and question answering scenarios, significantly lagging behind human performance.
• Role of Contextual Information: There’s some success in using relevant textual or narrative contexts to improve time series forecasts.
• First-of-its-kind Framework: A unique framework designed for evaluating time series reasoning abilities in language models is introduced.
• Key Task Areas Explored: Testing includes etiological reasoning, factual question answering, and context-aided forecasting.

Opinion: This research underscores the critical importance of developing more sophisticated AI models that can not only analyze but also understand and reason over time series data. It opens up challenging avenues that could greatly benefit sectors like finance and healthcare, where predictive accuracy is crucial.