Multivariate Time Series Prediction Based on Optimized Temporal Convolutional Networks with Stacked Auto-encoders
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:157-172, 2019.
Multivariate time series prediction has recently attracted extensive research attention due to its wide applications in the area of financial investment, energy consumption, environmental pollution and so on. Because of the temporal complexity and nonlinearity existing in multivariate time series, few existing models could provide satisfactory prediction results. In this paper, we proposed a novel prediction approach based on optimized temporal convolutional networks with stacked auto-encoders, which can achieve better prediction performance as demonstrated in the experiments. Stacked auto-encoders are employed to extract effective features from complex multivariate time series. A temporal convolutional network is then constructed serving as the prediction model, which has a flexible receptive field and enjoys faster training speed with parallel computing ability than recurrent neural networks. The optimal hyperparameters in these models are discovered by Bayesian optimization. We performed extensive experiments by comparing the proposed algorithms and other popular algorithms on three different datasets, where the proposed approach obtain the best prediction results in various prediction horizons. In addition, we carefully analyze the search process of Bayesian optimization and provide further insights into hyperparametric tuning processes combining the exploration strategy with the exploitation strategy.