Xiaorui Shao1, Chang Soo Kim1, *, Dae Geun Kim2
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108
Abstract Time series classification (TSC) has attracted various attention in the
community of machine learning and data mining and has many successful applications
such as fault detection and product identification in the process of building a smart
factory. However, it is still challenging for the efficiency and accuracy of classification
due to complexity, multi-dimension of time series. This paper presents a new approach
for time series classification based on convolutional neural networks (CNN). The
proposed method contains three parts: short-time gap feature extraction, multi-scale local
feature learning, and global feature learning. In the process of short-time gap feature
extraction, large kernel… More >
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