Summary of From News to Forecast: Integrating Event Analysis in Llm-based Time Series Forecasting with Reflection, by Xinlei Wang et al.
From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
by Xinlei Wang, Maike Feng, Jing Qiu, Jinjin Gu, Junhua Zhao
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach combines Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by considering both text and time series data. It utilizes LLM-based agents to iteratively filter out irrelevant news, employing human-like reasoning to evaluate predictions. This enables the model to analyze complex events and continuously refine its output. The method integrates selected news events with time series data, fine-tuning a pre-trained LLM to predict sequences of digits in time series. The results show significant improvements in forecasting accuracy, suggesting a potential paradigm shift. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special machines that can understand language to help make better predictions about what will happen next in a pattern of numbers. It takes into account both the numbers and what’s happening in the world at the same time. This helps the machine learn from unexpected events and make more accurate predictions. The results show that this new way of predicting is much better than before, which could change how we forecast the future. |
Keywords
» Artificial intelligence » Fine tuning » Time series
