基于深度学习的股票价格预测

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion Pub Date : 2019-12-02 DOI:10.1145/3368235.3369367

Chunho Cho, Guan-Yi Lee, Yueh-Lin Tsai, Kun-Chan Lan

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引用次数: 11

摘要

本研究实现了LSTM、Seq2seq和WaveNet三种方法。我们比较了不同深度学习方法在预测股票价格方面的表现。我们使用预测价格与实际价格之间的相关性作为性能度量来评估这些方法的有效性。

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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Toward Stock Price Prediction using Deep Learning

Three methods including LSTM, Seq2seq and WaveNet are implemented in this study. We compare the performance of different deep learning methods in predicting stock prices. We use the correlation between the predicted price and the actual price as the performance metric to evaluate the effectiveness of these methods.

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Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion

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