基于EMD与DVG的非线性时间序列预测模型及其应用研究

针对具有非线性和不稳定性的时间序列，提出一种结合经验模态分解(EMD)、有向可见图(DVG)网络的动态预测模型。利用经验模态分解将原时间序列分解为多个固有模态函数(IMF),然后对分解后的高频和低频IMF利用快速傅里叶变换得到各自的周期；依据每个周期，从原时间序列的尾部截取长短不一的子序列，然后采用有向可见图算法转换为多个有向网络，利用随机游走在每个有向网络中寻找与时间序列最后一个节点相似的节点；最后，依据平行线法，预测时间序列的下一个数值。原油价格的时间序列是一类典型的具有非线性和不稳定性的序列，利用此模型对WTI原油每日价格进行实证分析。研究结果表明，此模型不但可以有效地预测时间序列的变化趋势，而且具有较高的预测精度。

Non-linear Time Series Prediction Model Based on EMD and DVG and Its Application

Nonlinear time series are often superimposed and coupled with multiple forms of changes such as trend changes, seasonal changes, cyclic fluctuations and random changes, which are manifested as cyclic changes of variable length and amplitude. For the time series with nonlinearity and instability, a dynamic prediction model combining empirical modal decomposition (EMD) and directed visible graph (DVG) network is proposed. First, the original time series is decomposed into multiple intrinsic mode functions (IMFs) by empirical modal decomposition, and then the decomposed high-frequency and low-frequency IMFs are used to obtain their respective periods by fast Fourier transform; based on each period, subsequences of varying lengths are intercepted from the tail of the original time series, and then the directed visible graph algorithm is used to convert the original time series into multiple directed networks, using random wandering in each directed network to find A node similar to the last node of the time series; finally, the next value of the time series is predicted based on the parallel line method. The time series of crude oil price is a typical non-linear and unstable series, and this model is used to empirically analyze the daily price of WTI crude oil time series. The research results show that this model can not only effectively predict the time series trend, but also has a high prediction accuracy. The prediction method proposed in this paper is based on the network method rather than the classical statistical method, which makes full use of the characteristics of the nodes themselves in the network and provides a new way of thinking for nonlinear time series prediction.