Securities Stock Price Prediction Based on K-means-LSTM Model
In view of the non-stationary and non-linear characteristics of stock data,traditional statistical models cannot accurately predict the future trend of stock prices.To address this problem,a hybrid deep learning method is constructed to improve prediction performance.Firstly,the distance algorithm is modified to DTW(dynamic time warping)by expanding the K-means clustering algorithm to K-means-DTW,which is more suitable for time series data,to cluster securities with similar price trends.Then,the LSTM(long short-term memory)model is trained through clustering data to predict the price of a single stock.Experimental results show that the hybrid model K-means-LSTM shows better prediction performance and its prediction accuracy and stability are better than the single LSTM model.
stock price predictionK-meansDTWK-means-LSTM(K-means-long short-term memory)hybrid model
万方数据
维普
