BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture

Abstract

Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear problems and only predicts data with a single column of time series. The gated recurrent unit neural network (GRU) can achieve the prediction of time series and nonlinear multivariate data, but a single nonlinear model does not yield optimal results. Therefore, a hybrid time series prediction model, BAG, combining linear and nonlinear characteristics of soil moisture, is proposed in this paper to achieve the identification process of linear and nonlinear relationships in soil moisture data so as to improve the accuracy of prediction results. In BAG, block Hankel tensor ARIMA (BHT-ARIMA) and GRU are selected to extract the linear and nonlinear features of soil moisture data, respectively. BHT-ARIMA is applied to predict the linear part of the soil moisture, and GRU is used to predict the residual series, which is the nonlinear part, and the superposition of the two predicted results is the final prediction result. The performance of the proposed model on five real datasets was evaluated. The results of the experiments show that BAG has a higher prediction accuracy compared with other prediction models for different amounts of data and different numbers of environmental factors.