摘要
时间序列预测在现代生活中广泛应用于众多领域,其重要性日益凸显。非平稳性是影响时间序列预测准确性的主要问题之一。时间序列数据的统计特性随时间变化,导致从历史数据中学习的规律难以被稳定地应用于未来预测,从而增加了预测的难度和不确定性。针对该问题,提出了一种新型框架StabilizeNet,旨在减少时间序列数据的非平稳性。该框架包括中心化和尺度变换、线性变换及逆序变换3个部分,通过引入可学习的归一化线性变换矩阵,优化数据的信息保留,并增强模型对时间序列动态的捕捉能力。与先进的时间序列预测模型Informer、SCINet、Pyraformer、FEDformer和Crossformer相比,StabilizeNet在多个数据集上展现出有效性和优越性。此框架为时间序列预测提供了一个新的预处理手段,有助于提升时间序列预测模型的预测性能。
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.
作者
安俊秀
万里浪
AN Junxiu;WAN Lilang(School of Software Engineering,Chengdu University of Information Technology,Chengdu 610225,China)
出处
《大数据》
2025年第2期127-139,共13页
Big Data Research
基金
国家社会科学基金项目(No.22XWB01214)。
关键词
时间序列预测
非平稳性
归一化
time series predication
non-stationarity
normalization
