Summary of Text2timeseries: Enhancing Financial Forecasting Through Time Series Prediction Updates with Event-driven Insights From Large Language Models, by Litton Jose Kurisinkel et al.
Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models
by Litton Jose Kurisinkel, Pruthwik Mishra, Yue Zhang
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: This paper presents a novel approach to time series forecasting in financial markets by incorporating textual information about relevant events into traditional numerical forecasts. The authors propose a collaborative modeling framework that leverages large language models’ intuition about future changes to update real number time series predictions. Specifically, the framework incorporates weighted averaging techniques over time intervals and sentiment analysis tasks to determine the impact of news events on stock prices. Evaluation metrics are used to assess the effectiveness of this approach on financial market data. The proposed method aims to provide more comprehensive and accurate time series predictions by considering both numerical and textual information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about a new way to predict how stock prices will change in the future. Right now, we use models that look at numbers from the past to make forecasts. But sometimes events outside of finance can affect stock prices, like news about politics or the economy. The authors are proposing a new approach that combines these two types of information – the numbers and the words – to make more accurate predictions. They tested their method on financial data and found it works well. This could help investors make better decisions and improve our understanding of how markets work. |
Keywords
» Artificial intelligence » Time series
