Summary of Collaborative Optimization in Financial Data Mining Through Deep Learning and Resnext, by Pengbin Feng et al.
Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt
by Pengbin Feng, Yankaiqi Li, Yijiashun Qi, Xiaojun Guo, Zhenghao Lin
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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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 This study proposes a ResNeXt-based multi-task learning framework to address the challenges of feature extraction and task collaborative optimization in financial data mining. The framework utilizes the group convolution mechanism of ResNeXt to efficiently extract local patterns and global features from high-dimensional, non-linear, and time-series financial data. To establish deep collaborative optimization relationships between multiple related tasks, the study introduces task sharing layers and dedicated layers. A flexible multi-task loss weight design allows for effective balancing of learning needs across different tasks and improves overall performance. The proposed framework is evaluated on a real S&P 500 financial dataset, demonstrating superior performance in classification and regression tasks compared to conventional deep learning models. The results highlight the framework’s effectiveness and robustness in handling complex financial data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to solve some big problems with how we analyze and understand financial data. Right now, our methods aren’t very good at dealing with really complicated data that has lots of different patterns and connections between things. The researchers came up with a new way to do this by using a special type of artificial intelligence called ResNeXt. They combined it with another technique called multi-task learning, which helps different tasks work together better. This new method is really good at finding the important information in financial data and making predictions about what will happen next. |
Keywords
» Artificial intelligence » Classification » Deep learning » Feature extraction » Multi task » Optimization » Regression » Time series
