Summary of Llmforecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data, by Hanyu Zhang et al.
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
by Hanyu Zhang, Chuck Arvin, Dmitry Efimov, Michael W. Mahoney, Dominique Perrault-Joncas, Shankar Ramasubramanian, Andrew Gordon Wilson, Malcolm Wolff
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, researchers introduce a novel approach called LLMForecaster to improve time-series forecasting models by incorporating rich unstructured information about the time series. The current state-of-the-art models often neglect this crucial aspect, leading to poor performance. The proposed method fine-tunes large language models (LLMs) to utilize semantic and contextual information, as well as historical data, to generate more accurate forecasts. This is demonstrated in an industry-scale retail application, where the technique achieves statistically significant improvements across several product sets experiencing holiday-driven demand surges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make time-series forecasting models better by using extra information about the things being forecasted. Right now, many models don’t take this information into account and that makes them worse at predicting what will happen in the future. The researchers developed a new method called LLMForecaster that uses large language models to look at extra details like what products are related to each other and what’s happening in the world. This helps make the predictions more accurate. They tested this approach on real-world retail data and showed that it works better than the current methods. |
Keywords
» Artificial intelligence » Time series
