TIMESFORMER:A MULTI-REPRESENTATION FRAMEWORK COMBINING CONVOLUTION AND TRANSFORMER FOR MULTIVARIATE TIME SERIES CLASSIFICATION TASKS

Authors: Ren Xiao, Zihua Xiong, Sheng Guo
Primary area: learning on time series and dynamical systems
Keywords: Time series classification

Abstract:
Multivariate time series classification is an important research area in machine learning, which is challenged by leveraging the inherent complexity of temporal, periodic, and multivariate dependencies.Classification models need to effectively extract features hidden within these dependencies, Such as the CNN-based model TimesNet, which effectively encodes periodic dependencies, performs well in classification tasks. However, its lack of effective encoding for multivariate dependencies limits its performance in classifying high-dimensional time series data. Additionally, due to the constraints of receptive fields, CNNs struggle to capture long-term dependencies in excessively long sequences. To address these issues, we propose a multi-representation framework that applies both point-wise and patch-wise temporal representations to multivariate time series data. This allows the model to leverage CNNs for capturing periodic dependencies while using transformer to capture global features across time and variable dimensions. In this way, we overcome the CNN's limitations in capturing long-term dependencies and integrate cross-variable dependencies into the model. Experimental results show that our approach achieves higher average accuracy than 17 of the latest time series classification models across 10 EUA classification datasets.

Decision: reject

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