Research on Stock Prediction Based on CED-PSO-StockNet Time Series Model

Research on Stock Prediction Based on CED-PSO-StockNet Time Series Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Stock Prediction Based on CED-PSO-StockNet Time Series Model Xinying Chen, Fengjiao Yang, Qianhan Sun, Weiguo Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4683223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 23 You are reading this latest preprint version Abstract In view of the complexity and uncertainty of the stock market, especially the noise interference in the stock data, the traditional single prediction method has been difficult to meet the needs of investors. This paper innovatively proposes the CED-PSO-StockNet time series model to improve the accuracy of stock forecasting. The model first introduces the complete ensemble empirical mode decomposition (CEEMDAN) technology, decomposes the original stock data, estimates the frequency of each component through the extreme point method, and recombines it, so as to effectively remove the noise. Then, the model uses the Encoder-Decoder framework which integrates the attention mechanism to accurately predict the reconstructed components, so as to better extract and use the data features. In addition, this paper also uses the improved particle swarm optimization algorithm to optimize the model parameters. Through five groups of comparative experiments, the effectiveness of each part of CED-PSO-StockNet model is verified, showing its significant advantages in stock forecasting. Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Computational biology and bioinformatics/Machine learning stock prediction CEEMDAN Encoder-Decoder Attention mechanism PSO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Jul, 2024 Reviews received at journal 29 Jul, 2024 Reviews received at journal 24 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviews received at journal 23 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviews received at journal 23 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviews received at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 22 Jul, 2024 Editor invited by journal 07 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 03 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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