TY - GEN
T1 - Time Series Sequences Classification with Inception and LSTM Module
AU - Wang, Jun
AU - Wang, Wenfeng
AU - Wei, Shaoming
AU - Zeng, Yajun
AU - Luo, Feixiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Convolutional neural networks use parameter sharing to greatly reduce the number of weights. However, multi-channel feature maps greatly increase the amount of computation, and at the same time, it is difficult to continue to reduce the number of weights. The Inception module solves this problem by using global average pooling and network in network(NIN) architecture. We propose a deep neural network using the inception module and the LSTM module, using the inception module to reduce the computational complexity of the convolutional network, and using LSTM to preserve the internal timing characteristics of the time series dataset. At the same time, the sliding window method is used to simply augment the training data. The method was tested on the UCR time series classification archive, with a lower error rate than the baseline model.
AB - Convolutional neural networks use parameter sharing to greatly reduce the number of weights. However, multi-channel feature maps greatly increase the amount of computation, and at the same time, it is difficult to continue to reduce the number of weights. The Inception module solves this problem by using global average pooling and network in network(NIN) architecture. We propose a deep neural network using the inception module and the LSTM module, using the inception module to reduce the computational complexity of the convolutional network, and using LSTM to preserve the internal timing characteristics of the time series dataset. At the same time, the sliding window method is used to simply augment the training data. The method was tested on the UCR time series classification archive, with a lower error rate than the baseline model.
KW - Inception module
KW - LSTM module
KW - time series sequences
UR - https://www.scopus.com/pages/publications/85081977815
U2 - 10.1109/ICTA48799.2019.9012862
DO - 10.1109/ICTA48799.2019.9012862
M3 - 会议稿件
AN - SCOPUS:85081977815
T3 - 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2019 - Proceedings
SP - 51
EP - 55
BT - 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2019
Y2 - 13 November 2019 through 15 November 2019
ER -