External data sources such as weather or events can be integrated into the prompt mining process to provide additional context and enhance forecasting accuracy. One way to incorporate this external data is by including relevant information in the prompts generated for language-based forecasting. For example, when describing historical mobility patterns, prompts could include details about weather conditions (e.g., temperature, precipitation) or significant events (e.g., concerts, festivals) that may have influenced human movement.
By integrating external data sources into the prompts, language models can learn to consider these factors when generating forecasts. This enriched context allows for more comprehensive predictions that take into account a wider range of influencing variables beyond just numerical mobility data. Additionally, leveraging external data sources in prompt mining can help capture correlations between different factors and improve the overall forecasting performance.