Implicit Reasoning in Deep Time Series Forecasting

Understand

Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains.

• However, it remains unclear whether their success stems from a true understanding of temporal dynamics or simply from memorizing the training data.
• While implicit reasoning in language models has been studied, similar evaluations for time series models have been largely unexplored.
• This work takes an initial step toward assessing the reasoning abilities of deep time series forecasting models.