基于多特征计算、LASSO 特征选择和 Ca-LSTM 网络的多特征股价预测模型

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-19 DOI:10.1080/09540091.2023.2286188

Xiao Chen, Lei Cao, Zhi Cao, HongWei Zhang

引用次数: 0

摘要

本文探讨的是股票价格预测这一关键领域，因其具有重大的经济意义而备受个人投资者和机构的青睐。其固有的非平稳性和...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A multi-feature stock price prediction model based on multi-feature calculation, LASSO feature selection, and Ca-LSTM network

This paper addresses the crucial realm of stock price prediction, highly coveted by individual investors and institutions for its substantial economic implications. The inherent non-stationary and ...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.