Multivariate Time Series Predictor With Parameter Optimization and Feature Selection Based on Modified Binary Salp Swarm Algorithm
Weijie Ren, Dewei Ma, Min Han
Abstract
More and more time series data appear in various fields, and the prediction of multivariate time series has been the key to solve many industrial problems. Therefore, it is necessary to establish an accurate prediction model. As an efficient recursive neural network, an echo state network (ESN) model has been widely used in time series prediction. However, it usually faces the problem of how to choose suitable reservoir parameters for different applications. In addition, selecting the input feature set is also an important issue, which will affect the accuracy and computational efficiency of the prediction model. To solve these problems, the modified binary salp swarm algorithm-based optimization ESN (MBSSA-ESN) is proposed for multivariate time series prediction, which can simultaneously realize feature subset selection and parameter optimization. In order to verify the effectiveness of the proposed model, Beijing air quality index data are used for simulation and the key index PM2.5 is used as the target variable for experiment. Compared with several related methods, the proposed model achieves the best results in all evaluation indicators, indicating that the MBSSA-ESN model is competitive in the task of multivariate time series prediction.