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We are developing an advanced multi-modal forecasting system that utilizes both time series data and textual data, such as news articles, to predict future trends and events. This innovative system integrates advanced time series backbone models with large language models (LLMs), combining the strengths of statistical analysis and machine learning techniques.
The time series model analyzes historical numerical data to identify patterns, while the LLM processes and extracts insights from text data, providing a richer contextual understanding for the predictions. This integrated approach aims to enhance decision-making processes by delivering not only accurate forecasts, but also insightful explanations based on relevant textual information.
By correlating numerical trends with contextual data, our system offers a comprehensive understanding of the factors driving the predictions, thereby enabling more informed and strategic decisions.
Team Members: Haifeng Chen
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