文献类型：期刊文献

中文题名：基于时序分解和随机森林的时间序列多步预测算法

英文题名：Time Series Multi-Step Prediction Algorithm Based on Time Series Decomposition and Random Forest

作者：文家璇[1];王苗[1];刘济[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：6

起止页码：873

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金(62076095)。

语种：中文

中文关键词：时间序列预测;遗传算法;变分模态分解;随机森林;实时分解

外文关键词：time series prediction;genetic algorithm;variational modal decomposition;random forests;real-time decomposition

摘要：时间序列多步预测利用事物的历史时间序列数据,对其未来多个时间点的发展趋势进行预报,以便提前制定相应的生产控制策略。提出一种新的基于时序分解的多步预测算法,针对时序分解参数对预测结果影响显著的问题,提出采用遗传算法优化的自适应变分模态分解策略;针对模态各异的子序列,提出运用随机森林算法建立多个基学习器以充分挖掘各模态信息;针对一次分解策略仅适用于仿真研究而无法实际应用的问题,提出实时分解框架并将整个分解与预测过程嵌入该框架中。多个公开时间序列数据集的实验结果表明所提出的预测算法相较于对比算法具有更高的精度。
The multi-step prediction of time series uses historical time series data to forecast their future development trends at multiple time points,in order to formulate corresponding production control strategies in advance.In this paper,a new multi-step prediction algorithm based on time series decomposition is proposed.To address the significant impact of time series decomposition parameters on prediction results,an adaptive variational mode decomposition strategy optimized by genetic algorithm is proposed.For sub-series with different modalities,a random forest algorithm is proposed to establish various base learners to fully mine the information of each sub-series.A real-time decomposition framework is proposed to address the problem that the one-time decomposition strategy is only applicable for simulation research and cannot be applied in practice.The whole decomposition and prediction process is embedded in this framework.Finally,experiments on several public time series datasets have shown that the proposed prediction algorithm has higher accuracy compared to the comparison algorithms.Although the final prediction result seems to has lower accuracy than the ones in some literatures,it is more feasible and practical.