TRNN: An efficient time-series recurrent neural network for stock price prediction

Prediction results in big data analysis can vary greatly depending on the data preprocessing methods used. Time series-based processing methods are particularly advantageous for prediction. While popular neural network models such as Back Propagation (BP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) are based on weight, loss function, and other factors, their training efficiency is still relatively low. In this paper, we propose an efficient Time-series Recurrent Neural Network (TRNN) for stock price prediction. In the proposed model, trading volume is established and sliding windows are used to process the time series data. The trends and turning points of the data are extracted according to financial market features, and data compression is achieved. To improve the impact of recent trading volume on the current stock price, the price-volume relationship is upgraded from one dimension to two dimensions based on RNN. The information about trading volume is processed and compressed to establish the TRNN model, which guarantees both accuracy and efficiency. We compare our TRNN model with the original RNN and LSTM models in terms of efficiency and accuracy. We further discuss the feasibility of related expanded schemes of our TRNN model, as well as the extendability of the time-series compression and TRNN model to other fields.