Abstract
Deep learning has revolutionized many areas, including time series data mining. Multivariate time series classification (MTSC) remained to be a well-known problem in the time series data mining community, due to its availability in various practical applications such as healthcare, finance, geoscience, and bioinformatics. Recently, multivariate long short-term memory with fully convolutional network (MLSTM-FCN) and multivariate attention long short-term memory with fully convolutional network (MALSTM-FCN) have shown superior results over various state-of-the-art methods. So, in this paper, we explore the usage of recurrent neural network (RNN), and its variants, such as bidirectional recurrent neural network (BiRNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU). We augment these RNN variants separately by replacing long short-term memory (LSTM) in MLSTM-FCN, which is the combination of LSTM, squeeze-and-excitation (SE) block, and fully convolutional network (FCN). Moreover, we integrate the SE block within FCN to leverage its high performance for the MTSC task. The resulting algorithms do not require heavy pre-processing or feature crafting. Thus, they could be easily deployed on real-time systems. We conduct a comprehensive evaluation with a large number of standard datasets and demonstrate that our approaches achieve notable results over the current best MTSC approach.
Similar content being viewed by others
References
Fawaz HI et al (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963
De Gooijer JG, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22(3):443–473
Schäfer P, Leser U (2017) Multivariate time series classification with WEASEL+ MUSE. arXiv preprint arXiv:1711.11343
Jaakkola T, Diekhans M, Haussler D (2000) A discriminative framework for detecting remote protein homologies. J Comput Biol 7(1–2):95–114
Van Der Maaten L (2011) Learning discriminative fisher kernels. in ICML
Orsenigo C, Vercellis C (2010) Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification. Pattern Recogn 43(11):3787–3794
Pei W, Dibeklioglu H, Tax DMJ, van der Maaten L (2017) Multivariate time-series classification using the hidden-unit logistic model. IEEE Transactions on Neural Networks and Learning Systems 29(4):920–931
Baydogan MG, Runger G (2015) Learning a symbolic representation for multivariate time series classification. Data Min Knowl Disc 29(2):400–422
Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification. arXiv preprint arXiv:1503.05018
Tuncel KS, Baydogan MG (2018) Autoregressive forests for multivariate time series modeling. Pattern Recogn 73:202–215
Zheng Y, et al. (2014) Time series classification using multi-channels deep convolutional neural networks. in International Conference on Web-Age Information Management. 2014. Springer
Karim F, Majumdar S, Darabi H, Harford S (2019) Multivariate lstm-fcns for time series classification. Neural Netw 116:237–245
Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. in 2017 International Joint Conference on Neural Networks (IJCNN). IEEE
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. in Proceedings of the 27th international conference on machine learning (ICML-10)
Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. in Proceedings of the IEEE conference on computer vision and pattern recognition
He K, et al. (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. in Proceedings of the IEEE international conference on computer vision
Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019
Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Zhao Y, Yang R, Chevalier G, Shah RC, Romijnders R (2018) Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction. Optik 158:266–272
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610
Chung J, et al. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555
Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958
Dua, DAG, Casey (2017) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Chollet F (2015) Keras. Available from: https://github.com/fchollet/keras
Abadi M, et al. (2016) Tensorflow: a system for large-scale machine learning. in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)
Funding
This paper was partially supported by the NSFC grant U1509216, U1866602, 61602129, and Microsoft Research Asia.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khan, M., Wang, H., Ngueilbaye, A. et al. End-to-end multivariate time series classification via hybrid deep learning architectures. Pers Ubiquit Comput 27, 177–191 (2023). https://doi.org/10.1007/s00779-020-01447-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-020-01447-7