Price prediction in China stock market: an integrated method based on time series clustering and image feature extraction.

Stock time-series data has the characteristics of high dimensionality and nonlinearity, which brings great challenges to stock forecasting. Aiming at the impact of stock correlation and the prediction information contained in stock image features, we propose a long short-term memory model based on clustering and image feature extraction, named Kmeans-CAE-LSTM. Firstly, the Kmeans algorithm is used for stock clustering, where the most correlated stocks are found. Secondly, a convolutional autoencoder (CAE) is applied to extract stock price image features. Finally, the stock technical data and image features are respectively input into the double-layer long-term short-term memory network to predict the stock price of the next trading day. The empirical research results on 11 industries in China's stock market show that the hybrid model has achieved the best prediction effect, which further proves the predictive ability of stock image data and can provide investors with new ideas for stock prediction and asset portfolio. [ABSTRACT FROM AUTHOR]