Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling
Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Jiawei [verfasserIn] Song, Pengyu [verfasserIn] Zhao, Chunhui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 145 |
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| Übergeordnetes Werk: |
volume:145 |
| DOI / URN: |
10.1016/j.patcog.2023.109943 |
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| Katalog-ID: |
ELV064983242 |
|---|
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| 245 | 1 | 0 | |a Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling |
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| 520 | |a Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. | ||
| 650 | 4 | |a Multi-rate time series | |
| 650 | 4 | |a Multi-scale temporal dynamics | |
| 650 | 4 | |a Label scarcity | |
| 650 | 4 | |a Temporal alignment | |
| 650 | 4 | |a Self-supervised learning | |
| 650 | 4 | |a Segment-wise mask autoencoding | |
| 700 | 1 | |a Song, Pengyu |e verfasserin |0 (orcid)0000-0003-3681-2310 |4 aut | |
| 700 | 1 | |a Zhao, Chunhui |e verfasserin |4 aut | |
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10.1016/j.patcog.2023.109943 doi (DE-627)ELV064983242 (ELSEVIER)S0031-3203(23)00641-6 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Chen, Jiawei verfasserin (orcid)0000-0001-7054-7974 aut Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. Multi-rate time series Multi-scale temporal dynamics Label scarcity Temporal alignment Self-supervised learning Segment-wise mask autoencoding Song, Pengyu verfasserin (orcid)0000-0003-3681-2310 aut Zhao, Chunhui verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 145 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 145 |
| spelling |
10.1016/j.patcog.2023.109943 doi (DE-627)ELV064983242 (ELSEVIER)S0031-3203(23)00641-6 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Chen, Jiawei verfasserin (orcid)0000-0001-7054-7974 aut Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. Multi-rate time series Multi-scale temporal dynamics Label scarcity Temporal alignment Self-supervised learning Segment-wise mask autoencoding Song, Pengyu verfasserin (orcid)0000-0003-3681-2310 aut Zhao, Chunhui verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 145 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 145 |
| allfields_unstemmed |
10.1016/j.patcog.2023.109943 doi (DE-627)ELV064983242 (ELSEVIER)S0031-3203(23)00641-6 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Chen, Jiawei verfasserin (orcid)0000-0001-7054-7974 aut Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. Multi-rate time series Multi-scale temporal dynamics Label scarcity Temporal alignment Self-supervised learning Segment-wise mask autoencoding Song, Pengyu verfasserin (orcid)0000-0003-3681-2310 aut Zhao, Chunhui verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 145 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 145 |
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10.1016/j.patcog.2023.109943 doi (DE-627)ELV064983242 (ELSEVIER)S0031-3203(23)00641-6 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Chen, Jiawei verfasserin (orcid)0000-0001-7054-7974 aut Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. Multi-rate time series Multi-scale temporal dynamics Label scarcity Temporal alignment Self-supervised learning Segment-wise mask autoencoding Song, Pengyu verfasserin (orcid)0000-0003-3681-2310 aut Zhao, Chunhui verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 145 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 145 |
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10.1016/j.patcog.2023.109943 doi (DE-627)ELV064983242 (ELSEVIER)S0031-3203(23)00641-6 DE-627 ger DE-627 rda eng 000 150 VZ 54.74 bkl Chen, Jiawei verfasserin (orcid)0000-0001-7054-7974 aut Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. Multi-rate time series Multi-scale temporal dynamics Label scarcity Temporal alignment Self-supervised learning Segment-wise mask autoencoding Song, Pengyu verfasserin (orcid)0000-0003-3681-2310 aut Zhao, Chunhui verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 145 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 145 |
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multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling |
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Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling |
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Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. |
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Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. |
| abstract_unstemmed |
Deep sequential networks have shown great power in time series regression and classification. So far, most approaches naturally assume that the time sequential data are uniformly sampled. In practice, however, different variables usually have different sampling rates, thereby forming multi-rate time series (MR-TS). Particularly, the target variable (i.e., label) to be predicted usually has a lower sampling frequency due to the difficulty of manual annotations. The multi-rate problem poses two challenges. One is the diverse dynamics at different sampling rates, which is defined as multi-scale dynamics. The other is label scarcity. To tackle the above obstacles, this paper developed a Multi-scale Self-supervised Representation Learning technique with a Temporal Alignment mechanism (MSRL-TA) as a coherent framework. Concretely, a probabilistic masked autoencoding approach is pertinently developed, in which segment-wise masking schemes and rate-aware positional encodings are devised to enable the characterization of multi-scale temporal dynamics. In the course of pre-training, the encoder networks are able to generate rich and holistic representations of multi-rate data, thereby alleviating the label scarcity issue for supervised fine-tuning. Furthermore, a Temporal Alignment mechanism is devised to refine synthesized features for dynamic predictive modeling through feature block division and block-wise convolution. With empirical evaluation through extensive experiments, our proposed MSRL-TA achieved consistent state-of-the-art in both multi-rate time series regression and classification tasks on five real-world datasets, including air quality prediction, industrial soft sensing, human activity recognition, and speech voice classification. |
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|
| score |
7.403097 |