Summary of Higher Order Transformers: Enhancing Stock Movement Prediction on Multimodal Time-series Data, by Soroush Omranpour et al.
Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data
by Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST)
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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 proposed Higher Order Transformers architecture is a novel approach to predicting stock movements by capturing complex market dynamics across time and variables. By extending the self-attention mechanism and transformer architecture to higher orders, the model effectively processes multivariate time-series data. To mitigate computational complexity issues, the authors introduce tensor decomposition and kernel attention techniques. The paper also presents an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Experimental results on the Stocknet dataset demonstrate the effectiveness of the proposed method in enhancing stock movement prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new type of computer program to predict how stock prices will change. The program, called Higher Order Transformers, can look at many different factors that affect the stock market and make predictions based on what it sees. The authors made some changes to the way the program works to help it process all this information efficiently. They also showed that their program is better than other programs at making these predictions. |
Keywords
» Artificial intelligence » Attention » Encoder decoder » Self attention » Time series » Transformer
