Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder
In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in unlabeled data for richer information. Contrastiv...
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| Vydáno v: | IEEE open journal of signal processing Ročník 5; s. 964 - 975 |
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| Jazyk: | angličtina |
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2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2644-1322, 2644-1322 |
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| Abstract | In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in unlabeled data for richer information. Contrastive self-supervised learning, particularly, has gained attention for time series classification. However, it introduces inductive bias by generating positive and negative samples. Another approach involves Masked Autoencoders (MAE), which are effective for various data types. However, due to their reliance on the Transformer architecture, they demand significant computational resources during the pre-training phase. Recently, inspired by the remarkable advancements achieved by convolutional networks in the domain of time series forecasting, we aspire to employ convolutional networks utilizing a strategy of mask recovery for pre-training time series models. This study introduces a novel model termed Hierarchical Sparse Convolutional Masked-Autoencoder, "HSC-MAE", which seamlessly integrates convolutional operations with the MAE architecture to adeptly capture time series features across varying scales. Furthermore, the HSC-MAE model incorporates dedicated decoders that amalgamate global and local information, enhancing its capacity to comprehend intricate temporal patterns. To gauge the effectiveness of the proposed approach, an extensive array of experiments was conducted across nine distinct datasets. The experimental outcomes stand as a testament to the efficacy of HSC-MAE in effectively mitigating the aforementioned challenges. |
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| AbstractList | In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in unlabeled data for richer information. Contrastive self-supervised learning, particularly, has gained attention for time series classification. However, it introduces inductive bias by generating positive and negative samples. Another approach involves Masked Autoencoders (MAE), which are effective for various data types. However, due to their reliance on the Transformer architecture, they demand significant computational resources during the pre-training phase. Recently, inspired by the remarkable advancements achieved by convolutional networks in the domain of time series forecasting, we aspire to employ convolutional networks utilizing a strategy of mask recovery for pre-training time series models. This study introduces a novel model termed Hierarchical Sparse Convolutional Masked-Autoencoder, "HSC-MAE", which seamlessly integrates convolutional operations with the MAE architecture to adeptly capture time series features across varying scales. Furthermore, the HSC-MAE model incorporates dedicated decoders that amalgamate global and local information, enhancing its capacity to comprehend intricate temporal patterns. To gauge the effectiveness of the proposed approach, an extensive array of experiments was conducted across nine distinct datasets. The experimental outcomes stand as a testament to the efficacy of HSC-MAE in effectively mitigating the aforementioned challenges. |
| Author | Xu, Kele Wang, Xu Ding, Bo Feng, Dawei Yu, Ting |
| Author_xml | – sequence: 1 givenname: Ting orcidid: 0009-0004-4451-2275 surname: Yu fullname: Yu, Ting organization: College of Computer, National University of Defense Technology, Changsha, China – sequence: 2 givenname: Kele orcidid: 0000-0001-5997-5169 surname: Xu fullname: Xu, Kele email: xukelele@163.com organization: College of Computer, National University of Defense Technology, Changsha, China – sequence: 3 givenname: Xu orcidid: 0009-0007-0006-7781 surname: Wang fullname: Wang, Xu organization: College of Computer, National University of Defense Technology, Changsha, China – sequence: 4 givenname: Bo orcidid: 0000-0002-1236-8318 surname: Ding fullname: Ding, Bo organization: College of Computer, National University of Defense Technology, Changsha, China – sequence: 5 givenname: Dawei orcidid: 0000-0002-7587-8905 surname: Feng fullname: Feng, Dawei organization: College of Computer, National University of Defense Technology, Changsha, China |
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| SubjectTerms | Classification Computer architecture Convolution Convolutional neural networks Decoders Decoding Effectiveness Machine learning Self-supervised learning Task analysis Time series Time series analysis Time series classification time series pre-training Transformers |
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| Title | Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder |
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