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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Stock Price Prediction Using Time Convolution Long Short-Term Memory Network
verfasst von : Xukuan Zhan, Yuhua Li, Ruixuan Li, Xiwu Gu, Olivier Habimana, Haozhao Wang
Erschienen in: Knowledge Science, Engineering and Management
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Abstract
The time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. Inspired by Convolutional Neural Network (CNN), we make convolution on the time dimension to capture the long-term fluctuation features of stock series. To learn long-term dependencies of stock prices, we combine the time convolution with Long Short-Term Memory (LSTM), and propose a novel deep learning model named Time Convolution Long Short-Term Memory (TC-LSTM) networks. TC-LSTM can obtain the stock longer data dependence and overall change pattern. The experiments on two real market datasets demonstrate that the proposed model outperforms other three baseline models in the mean square error.