Article Dans Une Revue ACM Computing Surveys Année : 2024

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

Résumé

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.

Fichier principal
Vignette du fichier
3649448.pdf (889.62 Ko) Télécharger le fichier
Origine Publication financée par une institution
Licence

Dates et versions

hal-05093756 , version 1 (02-06-2025)

Licence

Identifiants

Citer

Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I Webb, Germain Forestier, et al.. Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. ACM Computing Surveys, 2024, 56 (9), pp.1 - 45. ⟨10.1145/3649448⟩. ⟨hal-05093756⟩

Collections

54 Consultations
211 Téléchargements

Altmetric

Partager

  • More