Deep learning for time series classification: a review. Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. Data Mining and Knowledge Discovery, 33(4):917–963, July, 2019. ![Deep learning for time series classification: a review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
@article{ismail_fawaz_deep_2019,
title = {Deep learning for time series classification: a review},
volume = {33},
issn = {1573-756X},
shorttitle = {Deep learning for time series classification},
url = {https://doi.org/10.1007/s10618-019-00619-1},
doi = {10.1007/s10618-019-00619-1},
abstract = {Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.},
language = {en},
number = {4},
urldate = {2020-12-12},
journal = {Data Mining and Knowledge Discovery},
author = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
month = jul,
year = {2019},
pages = {917--963},
}
Downloads: 0
{"_id":"te4FF6WzSRQij8rxh","bibbaseid":"ismailfawaz-forestier-weber-idoumghar-muller-deeplearningfortimeseriesclassificationareview-2019","author_short":["Ismail Fawaz, H.","Forestier, G.","Weber, J.","Idoumghar, L.","Muller, P."],"bibdata":{"bibtype":"article","type":"article","title":"Deep learning for time series classification: a review","volume":"33","issn":"1573-756X","shorttitle":"Deep learning for time series classification","url":"https://doi.org/10.1007/s10618-019-00619-1","doi":"10.1007/s10618-019-00619-1","abstract":"Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.","language":"en","number":"4","urldate":"2020-12-12","journal":"Data Mining and Knowledge Discovery","author":[{"propositions":[],"lastnames":["Ismail","Fawaz"],"firstnames":["Hassan"],"suffixes":[]},{"propositions":[],"lastnames":["Forestier"],"firstnames":["Germain"],"suffixes":[]},{"propositions":[],"lastnames":["Weber"],"firstnames":["Jonathan"],"suffixes":[]},{"propositions":[],"lastnames":["Idoumghar"],"firstnames":["Lhassane"],"suffixes":[]},{"propositions":[],"lastnames":["Muller"],"firstnames":["Pierre-Alain"],"suffixes":[]}],"month":"July","year":"2019","pages":"917–963","bibtex":"@article{ismail_fawaz_deep_2019,\n\ttitle = {Deep learning for time series classification: a review},\n\tvolume = {33},\n\tissn = {1573-756X},\n\tshorttitle = {Deep learning for time series classification},\n\turl = {https://doi.org/10.1007/s10618-019-00619-1},\n\tdoi = {10.1007/s10618-019-00619-1},\n\tabstract = {Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2020-12-12},\n\tjournal = {Data Mining and Knowledge Discovery},\n\tauthor = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {917--963},\n}\n\n\n\n","author_short":["Ismail Fawaz, H.","Forestier, G.","Weber, J.","Idoumghar, L.","Muller, P."],"key":"ismail_fawaz_deep_2019","id":"ismail_fawaz_deep_2019","bibbaseid":"ismailfawaz-forestier-weber-idoumghar-muller-deeplearningfortimeseriesclassificationareview-2019","role":"author","urls":{"Paper":"https://doi.org/10.1007/s10618-019-00619-1"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":[],"search_terms":["deep","learning","time","series","classification","review","ismail fawaz","forestier","weber","idoumghar","muller"],"title":"Deep learning for time series classification: a review","year":2019}