大数据2025，Vol.11Issue(2)：127-139,13.DOI:10.11959/j.issn.2096-0271.2025019
StabilizeNet:用于缓解时间序列非平稳性的新型框架
StabilizeNet:a novel framework for alleviating non-stationarity in time series
摘要
Abstract
Time series prediction is widely used in many fields in modern life,and its importance has become increasingly prominent.Non-stationarity is one of the main issues affecting the accuracy of time series predictions.Due to the statistical characteristics of time series data changing over time,it is difficult to stably apply the patterns learned from historical data to future predictions,thus increasing the difficulty and uncertainty of predictions.In order to solve this problem,a novel framework called StabilizeNet was proposed,which was designed to reduce the non-stationarity of time series data.The framework consisted of three parts:centralization and scaling transformation,linear transformation,and reverse transformation.By introducing a learnable normalized linear transformation matrix,it optimized data information retention and enhanced the model's ability to capture time series dynamics.Compared with advanced time series prediction models such as Informer,SCINet,Pyraformer,FEDformer,and Crossformer,StabilizeNet demonstrated effectiveness and superiority on multiple datasets.This framework provides a new preprocessing method for time series prediction,which helps to improve the prediction performance of time series predictive models.关键词
时间序列预测/非平稳性/归一化Key words
time series predication/non-stationarity/normalization分类
信息技术与安全科学引用本文复制引用
安俊秀,万里浪..StabilizeNet:用于缓解时间序列非平稳性的新型框架[J].大数据,2025,11(2):127-139,13.基金项目
国家社会科学基金项目(No.22XWB01214) The National Social Science Foundation of China(No.22XWB01214) （No.22XWB01214）
