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The application and investigation of dropout layer in the generator of GAN in stock prediction
- 1 Jilin University
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Abstract
Recent years have witnessed rapid advancements in neural network capabilities. Among the cutting-edge techniques emerging in this field is the Generative Adversarial Network (GAN).The GAN framework comprises two competing neural networks: one aims to introduce noise and deceive its counterpart, while the other hones its skills on genuine data, guiding the first on enhancing the realism of its noise. After extensive training, the goal is for the former network to produce data so convincing that the latter cannot differentiate between authentic and fabricated. The study’s objective is to harness this formidable technique to model time series data, with a primary focus on stock market dynamics. A GAN proficient in handling the complexities of time series data, notably unpredictable realms like the stock market, has vast potential applications. While GANs have proven adept at navigating the intricacies of time series data, they aren’t without challenges, notably the issue of overfitting. In this study, it will address this limitation by integrating a dropout layer into the generator. Such advancements in GAN could revolutionize the finance sector, offering improved risk evaluations for investments. Furthermore, GANs might hold the key to creating synthetic duplicates of confidential data, ensuring data veracity without jeopardizing confidentiality. In an era where vast amounts of data are locked away due to privacy concerns, the ability to generate precise, synthetic datasets could truly be groundbreaking.
Keywords
Machine Learning, GAN, Dropout, Stock Prediction
[1]. Hirano M Sakaji H and Izumi K 2022 Policy gradient stock gan for realistic discrete order data generation in financial markets arXiv preprint arXiv:2204.13338
[2]. Zhang K et al 2019 Stock market prediction based on generative adversarial network Procedia computer science 147 400-406
[3]. Qiu Y et al 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72 103323
[4]. Monil P et al 2020 Customer segmentation using machine learning. International Journal for Research in Applied Science and Engineering Technology (IJRASET) 8(6) 2104-2108
[5]. Hamedinia H Raei R Bajalan S and Rouhani S 2022 Analysis of Stock Market Manipulation using Generative Adversarial Nets and Denoising Auto-Encode Models Advances in Mathematical Finance and Applications 7(1) 149-167
[6]. Srivastava N et al 2014 Dropout: a simple way to prevent neural networks from overfitting The journal of machine learning research 15(1) 1929-1958
[7]. Chen T et al 2015 Xgboost: extreme gradient boosting R package version 0.4-2 1(4) 1-4
[8]. Chen T and Guestrin C 2016 Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining pp 785-794
[9]. Borji A 2019 Pros and cons of gan evaluation measures Computer vision and image understanding 179, 41-65
[10]. Creswell A et al 2018 Generative adversarial networks: An overview IEEE signal processing magazine 35(1) 53-65
Cite this article
Li,Z. (2024). The application and investigation of dropout layer in the generator of GAN in stock prediction. Applied and Computational Engineering,45,196-201.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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