A Review of Research on Time Series Classification Based on Deep Learning
Time Series Classification(TSC)is one of the most important and challenging tasks in the field of data mining.Deep learning techniques have achieved revolutionary progress in natural language processing and computer vision,and have also demonstrated great potential in areas such as time series analysis.A detailed review of the latest research advances in deep learning-based TSC is provided in this paper.Firstly,key terms and related concepts are defined.Secondly,the latest time series classification models are classified from four perspectives of network architectures:multilayer perceptron,convolutional neural networks,recurrent neural networks,and attention mechanisms,along with their respective advantages and limitations.Additionally,the latest developments and challenges in time series classification in the fields of human activity recognition and electroencephalogram-based emotion recognition are outlined.Finally,the unresolved issues and future research directions when applying deep learning to time series data are discussed.This paper provides researchers with a reference for understanding the latest research dynamics,new technologies,and development trends in the deep learning-based time series classification field.
Deep learningTime seriesNeural networksClassificationReview
NETL
NSTL
万方数据
