Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 268

The DOI matches its Crossref record; 273 of the 273 references it cites were found in a registry or an index.

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Open access
Mohammadi Foumani, Navid, Miller, Lynn, Tan, Chang Wei, Webb, Geoffrey I., Forestier, Germain, Salehi, Mahsa·2024·ACM Computing Surveys
DOI 10.1145/3649448
Open this paper in CoChat — free account

Abstract

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3649448

Cites

273 of 273 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 273 references

Research works printed but not linked: 38

  1. Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 857–876.
  2. Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen. 2021. Voice2series: Reprogramming acoustic models for time series classification. In International Conference on Machine Learning. PMLR, 11808–11819.
  3. Joan Serrà, Santiago Pascual, and Alexandros Karatzoglou. 2018. Towards a universal neural network encoder for time series. In CCIA. 120–129.
  4. 10.5555/2832747.2832806 · doi:10.5555/2832747.2832806
  5. Zhiguang Wang and Tim Oates. 2015. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the 29th AAAI Conference on Artificial Intelligence.
  6. Nima Hatami, Yann Gavet, and Johan Debayle. 2018. Classification of time-series images using deep convolutional neural networks. In 10th International Conference on Machine Vision (ICMV’17), Vol. 10696. SPIE, 242–249.
  7. Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data augmentation for time series classification using convolutional neural networks. In ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data.
  8. Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, and Prateek Jain. 2019. Shallow RNN: Accurate time-series classification on resource constrained devices. Advances in Neural Information Processing Systems 32 (2019), 11 pages.
  9. Santiago Fernández, Alex Graves, and Jürgen Schmidhuber. 2007. Sequence labelling in structured domains with hierarchical recurrent neural networks. In 20th International Joint Conference on Artificial Intelligence (IJCAI’07).
  10. Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In International Conference on Machine Learning. PMLR, 1310–1318.
  11. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).
  12. Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. 2015. Spatial transformer networks. Advances in Neural Information Processing Systems 28 (2015), 2017–2025.
  13. Tarek Allam Jr. and Jason D. McEwen. 2021. Paying attention to astronomical transients: Photometric classification with the time-series transformer. arXiv preprint:2105.06178 (2021). · arXiv
  14. Huaiyuan Liu Xianzhang Liu Donghua Yang Zhiyu Liang Hongzhi Wang Yong Cui and Jun Gu. 2023. TodyNet: Temporal dynamic graph neural network for multivariate time series classification. ArXiv abs/2304.05078 (2023). https://api.semanticscholar.org/CorpusID:258059979 · arXiv
  15. Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, and Ming Jack Po. 2019. Temporal graph convolutional networks for automatic seizure detection. In Machine Learning for Healthcare Conference. PMLR, 160–180.
  16. Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In ICML. 25038–25054.
  17. Sana Tonekaboni Danny Eytan and Anna Goldenberg. 2021. Unsupervised representation learning for time series with temporal neighborhood coding. International Conference on Learning Representations. https://openreview.net/forum?id=8qDwejCuCN
  18. Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. In Proceedings of Neural Information Processing Systems (NeurIPS’22).
  19. Bhavik Vachhani, Chitralekha Bhat, and Sunil Kumar Kopparapu. 2018. Data augmentation using healthy speech for dysarthric speech recognition. In Interspeech. 471–475.
  20. Jingkun Gao Xiaomin Song Qingsong Wen Pichao Wang Liang Sun and Huan Xu. 2020. RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks. arxiv:cs.LG/2002.09545 (2020).
  21. Zhicheng Cui Wenlin Chen and Yixin Chen. 2016. Multi-Scale Convolutional Neural Networks for Time Series Classification. arxiv:cs.CV/1603.06995 (2016).
  22. Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data augmentation for time series classification using convolutional neural networks. In ECML/PKDD on Advanced Analytics and Learning on Temporal Data.
  23. Gautier Pialla, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, and Germain Forestier. 2022. Data augmentation for time series classification with deep learning models. In International Workshop on Advanced Analytics and Learning on Temporal Data. Springer, 117–132.
  24. 10.1109/BigData.2018.8621990 · DOI · Paper page
  25. Stephan Spiegel. 2016. Transfer learning for time series classification in dissimilarity spaces. Proceedings of AALTD 78 (2016).
  26. 10.5555/2832747.2832806 · doi:10.5555/2832747.2832806
  27. 10.1016/j.rse.2007.07.004 · doi:10.1016/j.rse.2007.07.004
  28. 10.1016/B978-0-12-809254-5.00002-6 · doi:10.1016/b978-0-12-809254-5.00002-6
  29. 10.1109/CVPR42600.2020.01234 · DOI
  30. 10.3390/rs14174378 · doi:10.3390/rs14174378
  31. 10.1016/j.infrared.2019.103152 · doi:10.1016/j.infrared.2019.103152
  32. Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Fabrizio G. Ventola, and Floriana Esposito. 2017. End-to-end learning of deep spatio-temporal representations for satellite image time series classification. In DC@PKDD/ECML.
  33. 10.3390/rs11050523 · DOI
  34. 10.3390/rs12172814 · DOI
  35. Yoav Freund and Robert E. Schapire. 1996. Experiments with a new boosting algorithm. In 13th International Conference on Machine Learning.148–156.
  36. Matthieu Herrmann and Geoffrey I. Webb. 2021. Amercing: An intuitive, elegant and effective constraint for dynamic time warping. arXiv preprint:2111.13314 (2021). · arXiv
  37. Angus Dempster, Daniel F. Schmidt, and Geoffrey I. Webb. 2023. Hydra: Competing convolutional kernels for fast and accurate time series classification. Data Mining and Knowledge Discovery 37 (2023), 1–27.
  38. Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In ICML.

Cited by

See all 268

Metadata

Authors
Mohammadi Foumani, N., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2024-04-25
Volume
56
Issue
9
Pages
1-45
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3649448
OpenAlex
W4392199044
Cited by
268
References
273
FWCI
48.64
Type
journal article
Language
en
Open access
hybrid
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Australian Government Research Training Program

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. https://app.cochat.ai/papers/10.1145/3649448
The paper
Mohammadi Foumani, N., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024). Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. ACM Computing Surveys. https://doi.org/10.1145/3649448

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.