Summary of Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues, by Zhijian Xu et al.
Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues
by Zhijian Xu, Yuxuan Bian, Jianyuan Zhong, Xiangyu Wen, Qiang Xu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Text-Guided Time Series Forecasting (TGTSF), a novel approach to predicting future events based on both historical data and textual information. They propose TGForecaster, a model that combines these two types of data using cross-attention mechanisms, and evaluate its performance on four benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This work is important because it can help us make more accurate predictions about things like stock prices or weather patterns by considering not just what has happened in the past, but also any relevant information from news articles or other sources. The researchers show that their approach outperforms traditional methods and could be used to improve many different types of time series forecasting. |
Keywords
» Artificial intelligence » Cross attention » Time series
