"亲爱的，时间跨度很重要"：应用 CNN-LSTM 方法预测美国股票 ETF

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-08-30 DOI:10.1080/13504851.2024.2396550

Wenguang Lin

引用次数: 0

摘要

本文使用卷积神经网络和长短期记忆（CNN-LSTM）混合模型，研究了预测（或输入）窗口长度对 Tr...的预测准确性的影响。

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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“Darling, time horizon matters”: applying the CNN-LSTM method for predicting US equity ETFs

The paper uses a hybrid model of convolutional neural network and long short-term memory (CNN-LSTM) to examine the impact of the prediction (or input) window length on the prediction accuracy of tr...

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.