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

Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I. Webb, Germain Forestier +1 more
2/6/2023
cs.LGcs.AIcs.CV

No official code found. Best implementation: Navidfoumani/TSC_Survey (48 stars, last commit 2024-04-04).

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 paper 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.

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

484Dec 16, 20232 years ago

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Cite this paper

@article{foumani2023deep,
  title  = {Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey},
  author = {Navid Mohammadi Foumani and Lynn Miller and Chang Wei Tan and Geoffrey I. Webb and Germain Forestier and Mahsa Salehi},
  year   = {2023},
  eprint = {2302.02515},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.02515v2}
}

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