Temporal Convolutional Networks with RNN approach for chaotic time series prediction

Abstract

The prediction of chaotic time series, which constitutes many systems in the field of science and engineering, has recently become the focus of attention of researchers. Chaotic time series prediction is making future predictions about these systems using previously observed data for a nonlinear chaotic system with a known initial condition. Chaotic time series prediction can be applied in many fields such as weather forecasting, finance and stock markets. Many disciplines work on solving time series prediction problem, ranging from forecasting weather events days in advance to traders predicting the future of stocks. In recent studies, it has been observed that hybrid deep neural network methods give better performance in solving time series prediction problems and have gained popularity in order to benefit from the advantages of more than one method in solving such problems. In this study, a hybrid deep neural network architecture is proposed for chaotic time series prediction. The used hybrid approach includes both temporal convolutional network to extract low level features from input and recurrent neural network layers such as long short-term memory and gated recurrent units to capture temporal information. Simulations were carried out on nine different chaotic time series dataset which are obtained from Lorenz, Rössler and a Lorenz-like chaotic equation sets, and twenty-one electrocardiogram (ECG) recordings of patients with arrhythmias. In the benchmark study, in which twelve different methods, including classical machine learning, deep neural network and hybrid models were used, the proposed model achieved the best prediction performance with an average root-mean-square error (RMSE) value of 0.0022 for chaotic dataset and 0.0082 for ECG arrhythmia dataset. Performance evaluation metrics show that the proposed hybrid architecture can compete with the models in state-of-the-art studies in chaotic time series prediction.