CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting
Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, mo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xingyu [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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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 17 vom: 31. März, Seite 20191-20205 |
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| Übergeordnetes Werk: |
volume:53 ; year:2023 ; number:17 ; day:31 ; month:03 ; pages:20191-20205 |
| Links: |
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| DOI / URN: |
10.1007/s10489-023-04496-6 |
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| 520 | |a Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. | ||
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10.1007/s10489-023-04496-6 doi (DE-627)SPR053086449 (SPR)s10489-023-04496-6-e DE-627 ger DE-627 rakwb eng Wang, Xingyu verfasserin (orcid)0000-0002-9672-7620 aut CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. Time series forecasting (dpeaa)DE-He213 Time series decomposition (dpeaa)DE-He213 CNNs (dpeaa)DE-He213 Liu, Hui aut Yang, Zhihan aut Du, Junzhao aut Dong, Xiyao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 17 vom: 31. März, Seite 20191-20205 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:17 day:31 month:03 pages:20191-20205 https://dx.doi.org/10.1007/s10489-023-04496-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 17 31 03 20191-20205 |
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10.1007/s10489-023-04496-6 doi (DE-627)SPR053086449 (SPR)s10489-023-04496-6-e DE-627 ger DE-627 rakwb eng Wang, Xingyu verfasserin (orcid)0000-0002-9672-7620 aut CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. Time series forecasting (dpeaa)DE-He213 Time series decomposition (dpeaa)DE-He213 CNNs (dpeaa)DE-He213 Liu, Hui aut Yang, Zhihan aut Du, Junzhao aut Dong, Xiyao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 17 vom: 31. März, Seite 20191-20205 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:17 day:31 month:03 pages:20191-20205 https://dx.doi.org/10.1007/s10489-023-04496-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 17 31 03 20191-20205 |
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10.1007/s10489-023-04496-6 doi (DE-627)SPR053086449 (SPR)s10489-023-04496-6-e DE-627 ger DE-627 rakwb eng Wang, Xingyu verfasserin (orcid)0000-0002-9672-7620 aut CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. Time series forecasting (dpeaa)DE-He213 Time series decomposition (dpeaa)DE-He213 CNNs (dpeaa)DE-He213 Liu, Hui aut Yang, Zhihan aut Du, Junzhao aut Dong, Xiyao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 17 vom: 31. März, Seite 20191-20205 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:17 day:31 month:03 pages:20191-20205 https://dx.doi.org/10.1007/s10489-023-04496-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 17 31 03 20191-20205 |
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10.1007/s10489-023-04496-6 doi (DE-627)SPR053086449 (SPR)s10489-023-04496-6-e DE-627 ger DE-627 rakwb eng Wang, Xingyu verfasserin (orcid)0000-0002-9672-7620 aut CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. Time series forecasting (dpeaa)DE-He213 Time series decomposition (dpeaa)DE-He213 CNNs (dpeaa)DE-He213 Liu, Hui aut Yang, Zhihan aut Du, Junzhao aut Dong, Xiyao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 17 vom: 31. März, Seite 20191-20205 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:17 day:31 month:03 pages:20191-20205 https://dx.doi.org/10.1007/s10489-023-04496-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 17 31 03 20191-20205 |
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10.1007/s10489-023-04496-6 doi (DE-627)SPR053086449 (SPR)s10489-023-04496-6-e DE-627 ger DE-627 rakwb eng Wang, Xingyu verfasserin (orcid)0000-0002-9672-7620 aut CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. Time series forecasting (dpeaa)DE-He213 Time series decomposition (dpeaa)DE-He213 CNNs (dpeaa)DE-He213 Liu, Hui aut Yang, Zhihan aut Du, Junzhao aut Dong, Xiyao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 17 vom: 31. März, Seite 20191-20205 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:17 day:31 month:03 pages:20191-20205 https://dx.doi.org/10.1007/s10489-023-04496-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 17 31 03 20191-20205 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. 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cnformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting |
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CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting |
| abstract |
Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Improving long-term time series forecasting accuracy and efficiency is of great value for real-world applications. The main challenge in the long-term forecasting of multivariate time series is to accurately capture the local dynamics and long-term dependencies of time series. Currently, most approaches capture temporal dependencies and inter-variable dependencies in intertwined temporal patterns, which are unreliable. Moreover, models based on time series decomposition methods are still unable to capture both short- and long-term dependencies well. In this paper, we propose an efficient multivariate time series forecasting model CNformer with three distinctive features. (1) The CNformer is a fully CNN-based time series forecasting model. (2) In the encoder, the stacked dilated convolution as a built-in block is combined with the time series decomposition to extract the seasonal component of the time series. (3) The convolution-based encoder-decoder attention mechanism refines seasonal patterns in the decoder and captures complex combinations between different related time series. Owing to these features, our CNformer has lower memory and time overhead than models based on self-attention and the Auto-Correlation mechanism. Experimental results show that our model achieves state-of-the-art performance on four real-world datasets, with a relative performance improvement of 20.29%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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CNformer: a convolutional transformer with decomposition for long-term multivariate time series forecasting |
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https://dx.doi.org/10.1007/s10489-023-04496-6 |
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Liu, Hui Yang, Zhihan Du, Junzhao Dong, Xiyao |
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Liu, Hui Yang, Zhihan Du, Junzhao Dong, Xiyao |
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| doi_str |
10.1007/s10489-023-04496-6 |
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2024-07-03T16:58:11.467Z |
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| score |
7.399625 |