Summary of Advancing Financial Risk Prediction Through Optimized Lstm Model Performance and Comparative Analysis, by Ke Xu et al.
Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis
by Ke Xu, Yu Cheng, Shiqing Long, Junjie Guo, Jue Xiao, Mengfang Sun
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 LSTM-based approach optimizes the performance of financial risk prediction by leveraging Long Short-Term Memory models. The study begins by reviewing the architecture and algorithmic foundations of LSTMs before detailing the training process and hyperparameter tuning strategy. Experimental adjustments are made to network parameters, resulting in improved performance. Comparative experiments demonstrate the optimized LSTM model’s significant advantages over random forest, BP neural networks, and XGBoost in terms of AUC index, validating its efficiency and practicality in financial risk prediction, particularly with complex time series data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses a type of AI called LSTMs to predict risks in finance. It starts by explaining what LSTMs are and how they work, then shows how the model is trained and adjusted for better results. The study compares its approach to other methods and finds that it performs well, especially with complex data. |
Keywords
» Artificial intelligence » Auc » Hyperparameter » Lstm » Random forest » Time series » Xgboost
