2024/12/24 by Zhuohuan Hu, F. Richard Yu, Hu, Zhuohuan +8 · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #Blockchain Technology Applications and Security #E-commerce and Technology Innovations #FOS: Computer and information sciences #FOS: Economics and business #Impact of AI and Big Data on Business and Society #Machine Learning (cs.LG) #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2412.18202
openalex publication_date 2024/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction.