Time Series Prediction Method Based on Simplified LSTM Neural Network

To solve the problem that the standard long short-term memory (LSTM) neural network is time consuming and has high complexity for time series prediction, a simplified LSTM neural network was proposed and it was applied to time series prediciton. First, the structure of the standard LSTM neural network was simplified by coupling input gate and forget gate. Second, the inputs and bias were removed from dynamic equation of the gates to further simplify the parameters. Third, the gradient descent algorithm was utilized to update the parameters of the simplified LSTM neural network. Finally, the validity of the proposed model was demonstrated by two time series benchmark problems and the prediction of biochemical oxygen demand (BOD) mass concentration in the wastewater treatment process. The experimental results show that the training time is shortened and the computational complexity is reduced without significantly reducing the prediction accuracy, which makes an efficient time series prediction.