Multivariate Time Series Classification Based on MCNN-LSTMs Network

Multivariate time series data has high latitude, variable length, coupling, multi-scale and other characteristics. The existing multivariate time series classification methods often extract a single type of feature through complex artificial feature engineering or deep neural network, and do not fully exploit the multi-class features of multivariate time series. Therefore, this paper proposes an end-to-end multi-scale neural network model MCNN-LSTMs for multivariate time series classification. Firstly, using multi-scale entropy and Inceptions structure ideas, the subsequences of each channel are convolved in time dimension by using one-dimensional convolution kernels of different sizes to extract high-level multi-scale spatial abstract features. Secondly, the extracted multi-scale spatial features are input into the FC-LSTM network to further extract their temporal features, and then the temporal and spatial features of the captured features are fused. Finally, the fused features are input into the fully connected layer for classification. The model is tested and evaluated on multiple data sets. The experimental results show that the network model has better classification effect than the existing multiple representative time series classification methods.

[1]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[2]  Yannis Manolopoulos,et al.  Feature-based classification of time-series data , 2001 .

[3]  S. Dyer,et al.  Cubic-spline interpolation. 1 , 2001 .

[4]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[5]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[6]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[7]  Huanhuan Chen,et al.  Granger Causality for Multivariate Time Series Classification , 2017, 2017 IEEE International Conference on Big Knowledge (ICBK).

[8]  Arnaud Doucet,et al.  Autoregressive Kernels For Time Series , 2011, 1101.0673.

[9]  김정민,et al.  Cubic Spline Interpolation을 이용한 얼굴 영상의 단순화 , 2010 .

[10]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[11]  Yi Zheng,et al.  Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks , 2014, WAIM.

[12]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Fei-Fei Li,et al.  Visualizing and Understanding Recurrent Networks , 2015, ArXiv.

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Houshang Darabi,et al.  Multivariate LSTM-FCNs for Time Series Classification , 2018, Neural Networks.

[17]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.