南京师大学报（自然科学版）2025，Vol.48Issue(2)：91-101,11.DOI:10.3969/j.issn.1001-4616.2025.02.010
基于IFS-LCT-ViT的时间序列分类方法
Time Series Classification Method Based on IFS-LCT-ViT
摘要
Abstract
Currently,most of time series classification problems are analyzed from a one-dimensional perspective.Time series in a two-dimensional perspective have higher levels of data,which brings more space for research exploration,but there are fewer related studies.And the studies are basically a combination of Gramian Angular Field(GAF)and convolutional neural network models.In this paper,time series classification from the image perspective will be studied in depth.The related problems existing in current method will be optimized.Firstly,the Imbalance Factor Subtraction(IFS)method is proposed to solve the computational redundancy problem of the GAF algorithm.It replaces the trigonometric operation of GAF with the basic operation,which reduces the operation of the image generation process without losing the classification accuracy.Secondly,aiming at the problem of local preference in convolutional class models,the task of image recognition is given to the Vision Transformer(ViT).The overall features of the image are obtained by segmenting the temporal transition graph and then assigning the attention weight in the same way to all segmented subblocks.Finally,a lightweight convolutional token(LCT)adapted to ViT is proposed to extract the local features of the original sequence through one-dimensional convolution to compensate for the information loss caused by the simple hard segmentation of the image by ViT.Combining all of the above,IFS-LCT-ViT is proposed,and to verify the validity of the model,experiments were carried out on 11 datasets on the UCR website.Experimental results show that compared with GRU-FCN,TST,GADF-CNN,XCM,OSCNN and MultiRocket,the model obtains the highest accuracy of 85.9%,80.2%,68.2%,63.0,85.3%and 84.0%on six datasets,which proves the effectiveness of the model in time series classification tasks.关键词
时间序列分类/图像视角/不平衡因子/视觉自注意力网络/轻量卷积令牌Key words
time series classification/image perspective/imbalance factor subtraction/vision transformer/lightweight convolution token分类
计算机与自动化引用本文复制引用
杨思栋,王珂,刘兵,苏冰..基于IFS-LCT-ViT的时间序列分类方法[J].南京师大学报（自然科学版）,2025,48(2):91-101,11.基金项目
国家自然科学基金项目(62276266). （62276266）
