三支残差修正的时间序列预测

Time series prediction with three-way residual error amendment

时间序列预测是大数据发展背景下的重要研究课题,具有广泛的应用前景,其主要任务是根据时序数据反映的发展规律去推测未来某阶段的走势,但大多数预测模型未能充分考虑残差带来的影响,无法取得更优的预测结果 .提出一种三支残差修正的融合时序预测模型,能够有效地将残差圈定在一定范围内,提高时间序列的预测精度.首先,利用时间序列分解算法STL (Seasonal-Trend Decomposition Procedure Based on Loess)将时间序列分解为趋势项、周期项和余项;其次,针对分解后的三个分量,设计轻量级梯度提升机(Lightweight Gradient Boosting Machine,LightGBM)和时间卷积网络(Temporal Convolutional Network,TCN)的融合预测模型;最后,结合三支决策理论设计了三支残差修正算法,修正余项预测过程中产生的残差,进而修正时间序列的预测结果 .实验结果证明,提出的模型在绝大多数情况下优于其他对比模型,预测效果更好.

With the rise of big data as a backdrop,time series prediction is a significant research area with a wide range of potential applications.According to the development law reflected by time series data,the primary goal of time series prediction is to foretell the trend of a specific stage in future.Most prediction models fail to fully consider the impact of the residual error,which makes it difficult to obtain better prediction results.This paper proposes a fusion time series prediction model with three-way residual error amendment.This model effectively bounds the residual error within a certain range,thereby improves the prediction accuracy of time series.Firstly,the time series decomposition algorithm STL(Seasonal-Trend decomposition procedure based on Loess)is used to decompose the time series into trend item,seasonal item and remain item.Secondly,a fusion prediction model of lightweight gradient boosting machine(LightGBM)and temporal convolutional network(TCN)are designed for the three decomposed components.Thirdly,combined with the three-way decisions theory,the three-way residual error amendment algorithm is designed to correct the residual error generated in the prediction process of remain item.Finally,the time series prediction results are adjusted significantly and righteously.Experimental results show that the proposed model is superior to other models in the vast majority of cases and has better prediction effects.