计算机工程2025，Vol.51Issue(5)：154-165,12.DOI:10.19678/j.issn.1000-3428.0069143
基于预训练递归Transformer-Mixer的多维时间序列分类研究
Research on Multi-Dimensional Time Series Classification Based on the Pre-Trained Recursive Transformer-Mixer
摘要
Abstract
Multi-dimensional time series classification is widely used in industry,medical treatment,finance and other fields;it plays an important role in industrial product quality control,disease prediction,financial risk control and so on.Aiming at the problem that time dependence and spatial dependence of multi-dimensional time series are equally important,and that traditional multi-dimensional time series models only focus on a certain dimension of time or space,this paper proposes a multi-dimensional time series classification model based on the pre-trained recursive Transformer-Mixer PRTMMTSC.The model is based on a Transformer-Mixer module that can fully learn the temporal and spatial correlations of multi-dimensional time series.To further improve the classification performance,inspired by the anomaly detection model,the proposed model combines the pre-trained hidden layer features and the residual features,and uses the PolyLoss loss function for training.To reduce the number of model training parameters,the Transformer-Mixer module in the model is constructed recursively,so that the number of multi-layer trainable parameters is only the number of single-layer Transformer-Mixer parameters.The experimental results on the UEA datasets show that the performance of the proposed model is better than that of the contrast models.Compared with the TARNet model and the RLPAM model,the accuracy of proposed model has increased by 3.03％and 4.69％,respectively.Ablation experiments on the UEA and the IF steel inclusions defect classification further illustrate the effectiveness of the proposed pre-trained method,Transformer-Mixer module,residual information,and the PolyLoss loss function.关键词
多维时间序列分类/Transformer-Mixer模块/机器学习/预训练/IF钢夹渣缺陷预报Key words
multi-dimensional time series classification/Transformer-Mixer module/machine learning/pre-training/IF steel inclusions defect prediction分类
信息技术与安全科学引用本文复制引用
邓泽先,张云贵,张琳..基于预训练递归Transformer-Mixer的多维时间序列分类研究[J].计算机工程,2025,51(5):154-165,12.基金项目
国家重点研发计划(2020YFB1712803). （2020YFB1712803）
