中图分类号：TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.233969 中文引用格式： 王子祥，李颜娥，武斌，等. 基于EWT-ARIMA组合模型的银杏液流预测与因子关联分析[J]. 电子技术应用，2023，49(10)：89-95. 英文引用格式： Wang Zixiang，Li Yane，Wu Bin，et al. Ginkgo sap flow prediction based on EWT-ARIMA model and factor correlation analysis[J]. Application of Electronic Technique，2023，49(10)：89-95.
Ginkgo sap flow prediction based on EWT-ARIMA model and factor correlation analysis
Wang Zixiang1，2，3，Li Yan’e1，2，3，Wu Bin1，Xu Dayu1，Wu Bin1
(1.College of Mathematics and Computer Science， Zhejiang A & F University， Hangzhou 311300， China； 2.Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province， Hangzhou 311300，China； 3.China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment， Hangzhou 311300， China)
Abstract： Due to the comprehensive effect of environmental factors and growth mechanism, the sap flow often presents the characteristics of nonlinearity and high randomness, and it is often difficult to predict it accurately by a single prediction method. This paper proposes to introduce the empirical wavelet transform (EWT) method to decompose the nonlinear and highly random ginkgo sap flow data to obtain two sets of multi-resolution components, and the ARIMA model is used to predict the components respectively. According to the results, it is proposed that the EWT-ARIMA model can accurately predict the change trend of sap flow, and the model evaluation indicators MSE, MAE, MAPE, R2 are 11.05, 0.1640, 0.9599 and 0.9598, respectively, which are greatly improved compared with the single ARIMA model. In this paper, transfer entropy (TE) is also used to explore the causal reflection between environmental factors in time delay and ginkgo sap flow without model assumptions.
Key words : ginkgo sap flow prediction；empirical wavelet transform；ARIMA；transfer entropy；causal analysis
