mlsanityData to 14 Oct 2026

Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis

arXiv 2405.20603 ↗First posted 31 May 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter tuning strategy, and adjusts network parameters through experiments to improve performance. Comparative experiments show that the optimized LSTM model shows significant advantages in AUC index compared with random forest, BP neural network and XGBoost, which verifies its efficiency and practicability in the field of financial risk prediction, especially its ability to deal with complex time series data, which lays a solid foundation for the application of the model in the actual production environment.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 31 May 2024.

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11 of 38 references.

References · 11

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  3. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 56
  4. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  5. 2024Exploring Diverse Methods in Visual Question Answeringcited by 45
  6. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
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  8. 2020The value of text for small business default prediction: A deep learning approachcited by 20
  9. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  10. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15
  11. 2024Utilizing Large Language Models for Information Extraction from Real Estate Transactionscited by 8

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Cited by year

Citing papers per year: 2024: 5.

5 papers.

  1. 2024Attention Mechanism and Context Modeling System for Text Mining Machine Translationcited by 15
  2. 2024A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methodscited by 12
  3. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
  4. 2024Applying Conditional Generative Adversarial Networks for Imaging Diagnosiscited by 9
  5. 2024Exploiting Diffusion Prior for Out-of-Distribution Detectioncited by 7