A Stock Price Prediction Method based on LSTM and K-Means

Abstract

Artificial neural network, as a nonlinear mapping or adaptive power system made by linking neurons,which can effectively resolve problems such as gradient explosion in stock price prediction processing. Recurrent neural network (RNN) is a common model for processing stock time-series data and is suitable for stock data involving sequential machine learning tasks, but the prediction results are poor when using long time span or nonlinear data for prediction. To address the problems of low prediction accuracy of ordinary neural networks for stock data with poor linearity and the inability of a single LSTM model to show the recommendation level of a target stock, the paper proposes a deep learning factor integrated prediction model based on LSTM-K-Means.On this basis, a stock price prediction method based on long-term and short-term memory network namely LSTM and K-means clustering algorithm is proposed. The method is not only designed to model stock ups and downs at different levels of combinations, but also more intuitively identifies stocks with better ups through returns and volatilities. Through experimental verification, the stock price prediction method based on LSTM with K-Means proposed in this paper is effective.