CONTEXT IS KEY: A BENCHMARK FOR FORECASTING WITH ESSENTIAL TEXTUAL INFORMATION

Summary from the TLDR AI newsletter

The best and most commonly used time series forecasting methods rely entirely on numerical features associated with the time series. However, they rarely use textual or semantic information about the task itself (e.g., are you predicting electric prices or customer churn). When prompted with this textual information in context, language models surpass all tested time series forecasting methods substantially across a wide range of carefully decontaminated tasks.