Temporal representation learning for time series classification

Recent years have witnessed the exponential growth of time series data as the popularity of sensing devices and development of IoT techniques; time series classification has been considered as one of the most challenging studies in time series data mining, attracting great interest over the last two decades. According to the empirical evidences, temporal representation learning-based time series classification has more superiority of accuracy, efficiency and interpretability as compared to hundreds of existing time series classification methods. However, due to the high time complexity of feature process, the performance of these methods has been severely restricted. In this paper, we first presented an efficient shapelet transformation method to improve the overall efficiency of time series classification, and then, we further developed a novel enhanced recurrent neural network model for deep representation learning to further improve the classification accuracy. Experimental results on typical real-world datasets have justified the superiority of our models over several shallow and deep representation learning competitors.

Automated summary

Recent years have seen significant growth in time series data due to the popularity of sensing devices and IoT techniques, making time series classification one of the most challenging studies in data mining. Empirical evidence suggests that learning-based time series classification methods have advantages in accuracy, efficiency, and interpretability compared to traditional methods. However, the high time complexity of feature processing has limited the performance of these methods. This paper introduces an efficient shapelet transformation method and an enhanced recurrent neural network model for deep representation learning to improve the overall efficiency and accuracy of time series classification.