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Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction
Author(s) -
Xie Li,
Yu Sheng
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6282
Subject(s) - autoencoder , computer science , artificial intelligence , convolutional neural network , stock market , feature extraction , stock market prediction , unsupervised learning , machine learning , normalization (sociology) , time series , pattern recognition (psychology) , deep learning , data mining , econometrics , mathematics , paleontology , horse , sociology , anthropology , biology
Abstract Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN‐based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models. The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two‐dimensional data and data normalization. CAE network‐based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods.