Abstract
In this paper, we propose a deep spatial transformer network (DSTN) to classify the time series data. This DSTN model can avoid the distortion that may be caused when the time series data is transformed to 2D-data, as it has an adaptive feature extraction mechanism profit and can carry out affine transformation on 2D-data. It can also avoid the influence of this distortion on the subsequent convolutional neural networks. Thus, Experimental results show that this framework can effectively classify and predict complex multivariable time series data.
This work has been supported by the National Natural Science Foundation of China under Grant 61971050 and 2020 Industrial Internet Innovation and Development Project of P.R. China “comprehensive Security Defense Platform Project for Industrial/Enterprise Network”
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Hu, D., Hu, B., Li, Z., Xv, F. (2022). The Time Series Data Classification Method Based on Deep Spatial Transformer Network. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_82
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DOI: https://doi.org/10.1007/978-981-19-0390-8_82
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