Multivariate Time Series Prediction with PID based Residual Compensation
Multivariate time series prediction has fundamental importance to various practical domains. Many useful techniques have been proposed in literature for improving the accuracy and efficiency of prediction. However, due to the complicated dependency between time stamps and the interactive dependency between multiple time series, it is very difficult to pursue a high accuracy and alleviate the prediction error. In this paper, we propose a residual compensation scheme for multivariate time series prediction, where the prediction error is modeled by a PID based residual network (PID R) to cover various linear, cumulative and differential effects. To evaluate the effectiveness of the residual compensation, a hybrid structure integrating the Seq2Seq model with the classic vector auto regression (VAR) is built as an initial predictor. The proposed final predictor with residual compensation (FPRC) incorporates the initial predictor and PID R. Extensive experiments show that the FPRC achieves superior accuracy in comparison to state of the art methods and ablation analysis demonstrates the effectiveness of residual compensation.
2020 International Joint Conference on Neural Networks (IJCNN)