Recurrent neural networks integrate multiple graph operators for spatial time series prediction

Abstract For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean sp...
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Autor*in:	Peng, Bo [verfasserIn] Ding, Yuanming Xia, Qingyu Yang, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multivariate time series Graph neural networks Graph operator integrator Space dependence Time dependence

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.

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 21 vom: 18. Aug., Seite 26067-26078
Übergeordnetes Werk:	volume:53 ; year:2023 ; number:21 ; day:18 ; month:08 ; pages:26067-26078

Links:	Volltext

DOI / URN:	10.1007/s10489-023-04632-2

Katalog-ID:	SPR053493524

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520			\|a Abstract For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series.
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allfields	10.1007/s10489-023-04632-2 doi (DE-627)SPR053493524 (SPR)s10489-023-04632-2-e DE-627 ger DE-627 rakwb eng Peng, Bo verfasserin aut Recurrent neural networks integrate multiple graph operators for spatial time series prediction 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213 Ding, Yuanming aut Xia, Qingyu aut Yang, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 21 vom: 18. Aug., Seite 26067-26078 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078 https://dx.doi.org/10.1007/s10489-023-04632-2 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 21 18 08 26067-26078
spelling	10.1007/s10489-023-04632-2 doi (DE-627)SPR053493524 (SPR)s10489-023-04632-2-e DE-627 ger DE-627 rakwb eng Peng, Bo verfasserin aut Recurrent neural networks integrate multiple graph operators for spatial time series prediction 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213 Ding, Yuanming aut Xia, Qingyu aut Yang, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 21 vom: 18. Aug., Seite 26067-26078 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078 https://dx.doi.org/10.1007/s10489-023-04632-2 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 21 18 08 26067-26078
allfields_unstemmed	10.1007/s10489-023-04632-2 doi (DE-627)SPR053493524 (SPR)s10489-023-04632-2-e DE-627 ger DE-627 rakwb eng Peng, Bo verfasserin aut Recurrent neural networks integrate multiple graph operators for spatial time series prediction 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213 Ding, Yuanming aut Xia, Qingyu aut Yang, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 21 vom: 18. Aug., Seite 26067-26078 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078 https://dx.doi.org/10.1007/s10489-023-04632-2 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 21 18 08 26067-26078
allfieldsGer	10.1007/s10489-023-04632-2 doi (DE-627)SPR053493524 (SPR)s10489-023-04632-2-e DE-627 ger DE-627 rakwb eng Peng, Bo verfasserin aut Recurrent neural networks integrate multiple graph operators for spatial time series prediction 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213 Ding, Yuanming aut Xia, Qingyu aut Yang, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 21 vom: 18. Aug., Seite 26067-26078 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078 https://dx.doi.org/10.1007/s10489-023-04632-2 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 21 18 08 26067-26078
allfieldsSound	10.1007/s10489-023-04632-2 doi (DE-627)SPR053493524 (SPR)s10489-023-04632-2-e DE-627 ger DE-627 rakwb eng Peng, Bo verfasserin aut Recurrent neural networks integrate multiple graph operators for spatial time series prediction 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213 Ding, Yuanming aut Xia, Qingyu aut Yang, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 21 vom: 18. Aug., Seite 26067-26078 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078 https://dx.doi.org/10.1007/s10489-023-04632-2 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 21 18 08 26067-26078
language	English
source	Enthalten in Applied intelligence 53(2023), 21 vom: 18. Aug., Seite 26067-26078 volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078
sourceStr	Enthalten in Applied intelligence 53(2023), 21 vom: 18. Aug., Seite 26067-26078 volume:53 year:2023 number:21 day:18 month:08 pages:26067-26078
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multivariate time series Graph neural networks Graph operator integrator Space dependence Time dependence
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Peng, Bo @@aut@@ Ding, Yuanming @@aut@@ Xia, Qingyu @@aut@@ Yang, Yang @@aut@@
publishDateDaySort_date	2023-08-18T00:00:00Z
hierarchy_top_id	271180919
id	SPR053493524
language_de	englisch
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author	Peng, Bo
spellingShingle	Peng, Bo misc Multivariate time series misc Graph neural networks misc Graph operator integrator misc Space dependence misc Time dependence Recurrent neural networks integrate multiple graph operators for spatial time series prediction
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topic_title	Recurrent neural networks integrate multiple graph operators for spatial time series prediction Multivariate time series (dpeaa)DE-He213 Graph neural networks (dpeaa)DE-He213 Graph operator integrator (dpeaa)DE-He213 Space dependence (dpeaa)DE-He213 Time dependence (dpeaa)DE-He213
topic	misc Multivariate time series misc Graph neural networks misc Graph operator integrator misc Space dependence misc Time dependence
topic_unstemmed	misc Multivariate time series misc Graph neural networks misc Graph operator integrator misc Space dependence misc Time dependence
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title	Recurrent neural networks integrate multiple graph operators for spatial time series prediction
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title_full	Recurrent neural networks integrate multiple graph operators for spatial time series prediction
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author_browse	Peng, Bo Ding, Yuanming Xia, Qingyu Yang, Yang
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doi_str_mv	10.1007/s10489-023-04632-2
title_sort	recurrent neural networks integrate multiple graph operators for spatial time series prediction
title_auth	Recurrent neural networks integrate multiple graph operators for spatial time series prediction
abstract	Abstract For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. © 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. © 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 For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series. © 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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title_short	Recurrent neural networks integrate multiple graph operators for spatial time series prediction
url	https://dx.doi.org/10.1007/s10489-023-04632-2
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author2	Ding, Yuanming Xia, Qingyu Yang, Yang
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up_date	2024-07-03T19:55:17.239Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053493524</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231024065126.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231024s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-023-04632-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053493524</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-023-04632-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Peng, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Recurrent neural networks integrate multiple graph operators for spatial time series prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract For multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, spatial dependence and high nonlinearity simultaneously, so Euclidean space is no longer sufficient to describe them. graph neural network presents a vital idea to solve this problem by modelling multivariate time series as graphs. Using the nature of graphs makes it possible to capture the dependencies between multivariate time series. However, no graph structure can perfectly characterize the relationships among multivariate time series; the facts underlying multivariate time series are much more complex. Therefore, we propose an integrated model (iGoRNN), which improves the model’s understanding of the deep relationships of multivariate time series by fusing the information captured by multiple graph operators through an integrator with a specific structure. In addition, we conducted experiments on the Metr-LA and PeMS-BAY datasets. The experimental results show that the proposed model outperforms the baseline model in three evaluation metrics, MAE, MAPE and RMSE, and can forecast complex multivariate time series.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multivariate time series</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph operator integrator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Space dependence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time dependence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ding, Yuanming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xia, Qingyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Yang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991</subfield><subfield code="g">53(2023), 21 vom: 18. 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