Prediction model of energy market by long short term memory with random system and complexity evaluation

Abstract

Due to the frequent and violent fluctuation of energy futures prices, the investment risk of energy investors is increased. Forecasting energy futures prices has progressively become the focus of research. However, traditional prediction model only conducts forecasting based on historical data without considering the behavior of the market, resulting in poor accuracy. In this paper, the random time effective function that considers the timeliness of historical data and the random change of market environment is applied to the long short term memory model to establish a novel prediction model, which is denoted by long short term memory with random time effective function model (LSTMRT). LSTM model has the characteristics of selective memory and the internal influence of time series, which is very suitable for the prediction of price time series. Random time effective function can give different weights to historical data. Furthermore, using multiscale cross-sample entropy (MCSE) as an innovative method to reveal the performance of prediction. Finally, comparing with other models selected in this paper, error evaluations and statistical comparisons are utilized to demonstrate the advantages and superiority of the proposed model. LSTMRT model has the effect of random movement and keeps the trend fluctuation of the original nonlinear data, which makes the prediction more accurate and more credible.