A Bi-GRU-based encoder–decoder framework for multivariate time series forecasting

Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 28; H. 9-10; S. 6775 - 6786
Hauptverfasser:	Balti, Hanen, Ben Abbes, Ali, Farah, Imed Riadh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024 Springer Nature B.V
Schlagworte:	Accuracy Application of Soft Computing Artificial Intelligence Artificial neural networks Autoregressive models Big Data Climate change Coders Computational Intelligence Control Correlation coefficients Datasets Deep learning Drought Engineering Forecasting Forecasting techniques General circulation models Mathematical Logic and Foundations Mechatronics Multivariate analysis Neural networks Optimization Precipitation Rain Remote sensing Remote sensors Robotics Root-mean-square errors Sensors Support vector machines Time series Variables China Deep learning Encoder–decoder Multivariate time series Drought forecasting
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers: a temporal attention context layer, a gated recurrent unit (GRU)-based decoder component, and a bidirectional gated recurrent unit (Bi-GRU)-based encoder component. The proposed framework was evaluated using multivariate gathered from various sources in China (remote-sensing sensors, climate sensors, biophysical sensors, and so on). According to experimental results, the proposed framework outperformed the baseline methods in univariate and multivariate times series (TS) forecasting. The correlation coefficient of determination ( R 2 ), root-mean-squared error (RMSE), and the mean absolute error (MAE) were used for the evaluation of the framework performance. The R 2 , RMSE, and MAE are 0.94, 0.20, and 0.13, respectively, for EDFMTS. In contrast, the RMSE provided by autoregressive integrated moving average (ARIMA), PROPHET, long short-term memory (LSTM), GRU and convolutional neural network (CNN)-LSTM are 0.72, 0.92, 0.36, 0.40, and 0.27, respectively.
AbstractList	Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers: a temporal attention context layer, a gated recurrent unit (GRU)-based decoder component, and a bidirectional gated recurrent unit (Bi-GRU)-based encoder component. The proposed framework was evaluated using multivariate gathered from various sources in China (remote-sensing sensors, climate sensors, biophysical sensors, and so on). According to experimental results, the proposed framework outperformed the baseline methods in univariate and multivariate times series (TS) forecasting. The correlation coefficient of determination ( R 2 ), root-mean-squared error (RMSE), and the mean absolute error (MAE) were used for the evaluation of the framework performance. The R 2 , RMSE, and MAE are 0.94, 0.20, and 0.13, respectively, for EDFMTS. In contrast, the RMSE provided by autoregressive integrated moving average (ARIMA), PROPHET, long short-term memory (LSTM), GRU and convolutional neural network (CNN)-LSTM are 0.72, 0.92, 0.36, 0.40, and 0.27, respectively. Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers: a temporal attention context layer, a gated recurrent unit (GRU)-based decoder component, and a bidirectional gated recurrent unit (Bi-GRU)-based encoder component. The proposed framework was evaluated using multivariate gathered from various sources in China (remote-sensing sensors, climate sensors, biophysical sensors, and so on). According to experimental results, the proposed framework outperformed the baseline methods in univariate and multivariate times series (TS) forecasting. The correlation coefficient of determination (R2), root-mean-squared error (RMSE), and the mean absolute error (MAE) were used for the evaluation of the framework performance. The R2, RMSE, and MAE are 0.94, 0.20, and 0.13, respectively, for EDFMTS. In contrast, the RMSE provided by autoregressive integrated moving average (ARIMA), PROPHET, long short-term memory (LSTM), GRU and convolutional neural network (CNN)-LSTM are 0.72, 0.92, 0.36, 0.40, and 0.27, respectively.
Author	Ben Abbes, Ali Farah, Imed Riadh Balti, Hanen
Author_xml	– sequence: 1 givenname: Hanen orcidid: 0000-0003-2725-226X surname: Balti fullname: Balti, Hanen email: hanen.balti@ensi-uma.tn organization: Riadi Laboratory,ENSI, University of Manouba – sequence: 2 givenname: Ali orcidid: 0000-0001-5714-7562 surname: Ben Abbes fullname: Ben Abbes, Ali organization: Riadi Laboratory,ENSI, University of Manouba – sequence: 3 givenname: Imed Riadh surname: Farah fullname: Farah, Imed Riadh organization: Riadi Laboratory,ENSI, University of Manouba
BookMark	eNp9kM9KAzEQxoNUsFVfwNOC5-hkZ3fTPdbiPygIas8hm52U1Ha3JqnizXfwDX0St60geOhpPpjvN9_wDVivaRti7EzAhQCQlwEgB-CQIocyR8HLA9YXGSKXmSx7W51yWWR4xAYhzAFSIXPss6dRcuX47eOUVzpQnVBj2pr89-dXTVuVWK-X9N76l8S2PlmuF9G9ae90pCS6JSWBvKOwWZLRIbpmdsIOrV4EOv2dx2x6c_08vuOTh9v78WjCDYoyco25tFWlbVHnuiogR2OGaC1qYyXqNKttlVepqQopCIbUmWWhs1yQrYTVgMfsfHd35dvXNYWo5u3aN12kQpCIJRSl6FzpzmV8G4Inq1beLbX_UALUpjy1K0915altearsoOE_yLioo2ub6LVb7Edxh4Yup5mR__tqD_UDyUCH9g
CitedBy_id	crossref_primary_10_3390_en18133324 crossref_primary_10_1142_S0219519425400743 crossref_primary_10_1103_PhysRevD_111_024067 crossref_primary_10_1007_s12145_025_01791_3 crossref_primary_10_1007_s11063_025_11791_5 crossref_primary_10_1007_s00607_024_01373_z crossref_primary_10_1007_s10664_025_10674_6 crossref_primary_10_1109_ACCESS_2024_3443172 crossref_primary_10_1016_j_mex_2024_102800 crossref_primary_10_1109_JIOT_2024_3418993 crossref_primary_10_1007_s11269_024_03785_0 crossref_primary_10_32604_cmc_2024_058374 crossref_primary_10_1088_1742_6596_3007_1_012050 crossref_primary_10_3390_sym17071063
Cites_doi	10.1016/j.asoc.2023.110172 10.3390/w10111543 10.1175/2009JCLI2909.1 10.1016/j.scitotenv.2019.135139 10.3390/app9071345 10.3390/w11050977 10.1007/s00521-021-06505-6 10.1016/j.energy.2019.05.230 10.1007/s11227-022-04386-7 10.3390/a11090132 10.1080/00031305.2017.1380080 10.1016/j.neucom.2019.12.118 10.1016/j.ecoinf.2020.101136 10.1007/s00477-017-1437-5 10.1016/j.jhydrol.2017.12.044 10.1007/s00477-021-02078-x 10.1155/2022/5596676 10.1007/s12517-020-5234-y 10.1016/j.rser.2017.02.085 10.1016/j.engappai.2010.09.007 10.1007/s00703-019-00675-8 10.1007/s12517-020-06140-w 10.1609/aaai.v31i1.10735 10.1109/ICOTEN52080.2021.9493513 10.24963/ijcai.2021/225 10.1109/MWSCAS.2017.8053243 10.1109/ICMLA.2019.00020 10.1007/s11053-019-09512-6 10.1109/CSAIEE54046.2021.9543210 10.1109/YAC.2016.7804912
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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.
DBID	AAYXX CITATION 8FE 8FG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS
DOI	10.1007/s00500-023-09531-9
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central - New (Subscription) Technology collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China
DatabaseTitle	CrossRef Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: AAdvanced Technologies & Aerospace Database (subscription) url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1433-7479
EndPage	6786
ExternalDocumentID	10_1007_s00500_023_09531_9
GeographicLocations	China
GeographicLocations_xml	– name: China
GroupedDBID	-5B -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 1N0 1SB 203 29~ 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5VS 67Z 6NX 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K7- KDC KOV LAS LLZTM M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM P2P P9P PF0 PT4 PT5 QOS R89 R9I RIG RNI ROL RPX RSV RZK S16 S1Z S27 S3B SAP SDH SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7X Z7Y Z7Z Z81 Z83 Z88 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 8FE 8FG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c319t-a357fbbaf6d5ab6053cc83ff3acf73a24dfb5b2cb671e08e7fb76a451efb1fa03
IEDL.DBID	RSV
ISICitedReferencesCount	16
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001136826400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1432-7643
IngestDate	Wed Nov 05 01:55:11 EST 2025 Sat Nov 29 03:36:35 EST 2025 Tue Nov 18 21:24:47 EST 2025 Fri Feb 21 02:40:27 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	9-10
Keywords	Deep learning Encoder–decoder Multivariate time series Drought forecasting
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-a357fbbaf6d5ab6053cc83ff3acf73a24dfb5b2cb671e08e7fb76a451efb1fa03
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-5714-7562 0000-0003-2725-226X
PQID	3073390691
PQPubID	2043697
PageCount	12
ParticipantIDs	proquest_journals_3073390691 crossref_primary_10_1007_s00500_023_09531_9 crossref_citationtrail_10_1007_s00500_023_09531_9 springer_journals_10_1007_s00500_023_09531_9
PublicationCentury	2000
PublicationDate	20240500
PublicationDateYYYYMMDD	2024-05-01
PublicationDate_xml	– month: 5 year: 2024 text: 20240500
PublicationDecade	2020
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Heidelberg
PublicationSubtitle	A Fusion of Foundations, Methodologies and Applications
PublicationTitle	Soft computing (Berlin, Germany)
PublicationTitleAbbrev	Soft Comput
PublicationYear	2024
Publisher	Springer Berlin Heidelberg Springer Nature B.V
Publisher_xml	– name: Springer Berlin Heidelberg – name: Springer Nature B.V
References	Rhif, Ben Abbes, Martinez (CR20) 2020 Xu, Chen, Zhang (CR26) 2018; 557 Balti, Ben Abbes, Mellouli (CR2) 2020 CR16 Tak-chung (CR22) 2011; 24 CR12 CR10 Zhang, Li, Chen (CR30) 2017; 31 CR32 CR31 Docheshmeh Gorgij, Alizamir, Kisi (CR7) 2022; 34 Rhif, Abbes, Farah (CR19) 2019 Chen, Huang, Jin (CR4) 2012; 5 Taylor, Letham (CR23) 2018; 72 CR3 CR6 Du, Liu, Liu (CR8) 2018 Miao, Pan, Wang (CR18) 2019 Li, Chen, Wu (CR17) 2020; 132 CR29 CR25 Deb, Zhang, Yang (CR5) 2017; 74 Du, Li, Yang (CR9) 2020; 388 Rhif, Abbes, Martínez (CR21) 2023; 137 Jasim, Awchi (CR14) 2020 Zhang, Li, Chen (CR28) 2022 Hu, Liu, Hao (CR13) 2022; 78 Ahmed, Deo, Feng (CR1) 2022; 36 Hu, Wu, Li (CR11) 2018 Kim, Cho (CR15) 2019; 182 Vicente-Serrano, Beguería, López-Moreno (CR24) 2010; 23 Yang, Mou, Meng (CR27) 2020 A Docheshmeh Gorgij (9531_CR7) 2022; 34 9531_CR31 9531_CR10 Z Li (9531_CR17) 2020; 132 9531_CR32 9531_CR12 X Hu (9531_CR13) 2022; 78 9531_CR16 AI Jasim (9531_CR14) 2020 SJ Taylor (9531_CR23) 2018; 72 9531_CR6 M Rhif (9531_CR21) 2023; 137 H Balti (9531_CR2) 2020 AA Ahmed (9531_CR1) 2022; 36 TY Kim (9531_CR15) 2019; 182 F Tak-chung (9531_CR22) 2011; 24 J Chen (9531_CR4) 2012; 5 M Yang (9531_CR27) 2020 H Zhang (9531_CR28) 2022 Y Zhang (9531_CR30) 2017; 31 9531_CR25 L Xu (9531_CR26) 2018; 557 9531_CR29 9531_CR3 J Du (9531_CR8) 2018 M Rhif (9531_CR19) 2019 M Rhif (9531_CR20) 2020 SM Vicente-Serrano (9531_CR24) 2010; 23 S Du (9531_CR9) 2020; 388 C Deb (9531_CR5) 2017; 74 Q Miao (9531_CR18) 2019 C Hu (9531_CR11) 2018
References_xml	– volume: 137 start-page: 172 issue: 110 year: 2023 ident: CR21 article-title: Veg-W2TCN: a parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2023.110172 – year: 2018 ident: CR11 article-title: Deep learning with a long short-term memory networks approach for rainfall-runoff simulation publication-title: Water doi: 10.3390/w10111543 – volume: 23 start-page: 1696 issue: 7 year: 2010 end-page: 1718 ident: CR24 article-title: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index publication-title: J Clim doi: 10.1175/2009JCLI2909.1 – year: 2020 ident: CR27 article-title: Modeling the effects of precipitation and temperature patterns on agricultural drought in China from 1949 to 2015 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2019.135139 – year: 2019 ident: CR19 article-title: Wavelet transform application for/in non-stationary time-series analysis: a review publication-title: Appl Sci doi: 10.3390/app9071345 – year: 2019 ident: CR18 article-title: Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network publication-title: Water doi: 10.3390/w11050977 – volume: 5 start-page: 1260 issue: 4 year: 2012 end-page: 1268 ident: CR4 article-title: SPI-based drought characteristics analysis and prediction for Xiqiao Station in Yunnan Province, China publication-title: Disaster Adv – ident: CR16 – volume: 34 start-page: 2425 issue: 3 year: 2022 end-page: 2442 ident: CR7 article-title: Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06505-6 – ident: CR12 – volume: 182 start-page: 72 year: 2019 end-page: 81 ident: CR15 article-title: Predicting residential energy consumption using CNN-LSTM neural networks publication-title: Energy doi: 10.1016/j.energy.2019.05.230 – ident: CR10 – ident: CR6 – volume: 78 start-page: 12686 issue: 10 year: 2022 end-page: 12709 ident: CR13 article-title: Attention-based conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction publication-title: J Supercomput doi: 10.1007/s11227-022-04386-7 – ident: CR29 – year: 2018 ident: CR8 article-title: Study of precipitation forecast based on deep belief networks publication-title: Algorithms doi: 10.3390/a11090132 – volume: 72 start-page: 37 issue: 1 year: 2018 end-page: 45 ident: CR23 article-title: Forecasting at scale publication-title: Am Stat doi: 10.1080/00031305.2017.1380080 – ident: CR25 – volume: 388 start-page: 269 year: 2020 end-page: 279 ident: CR9 article-title: Multivariate time series forecasting via attention-based encoder–decoder framework publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.12.118 – year: 2020 ident: CR2 article-title: A review of drought monitoring with big data: issues, methods, challenges and research directions publication-title: Ecol Inform doi: 10.1016/j.ecoinf.2020.101136 – volume: 31 start-page: 2471 issue: 10 year: 2017 end-page: 2481 ident: CR30 article-title: Multi-models for SPI drought forecasting in the north of Haihe River Basin, China publication-title: Stoch Environ Res Risk Assess doi: 10.1007/s00477-017-1437-5 – ident: CR3 – volume: 557 start-page: 378 year: 2018 end-page: 390 ident: CR26 article-title: A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China publication-title: J Hydrol doi: 10.1016/j.jhydrol.2017.12.044 – volume: 36 start-page: 831 issue: 3 year: 2022 end-page: 849 ident: CR1 article-title: Hybrid deep learning method for a week-ahead evapotranspiration forecasting publication-title: Stoch Environ Res Risk Assess doi: 10.1007/s00477-021-02078-x – ident: CR31 – ident: CR32 – year: 2022 ident: CR28 article-title: A novel encoder–decoder model for multivariate time series forecasting publication-title: Comput Intell Neurosci doi: 10.1155/2022/5596676 – year: 2020 ident: CR14 article-title: Regional meteorological drought assessment in Iraq publication-title: Arab J Geosci doi: 10.1007/s12517-020-5234-y – volume: 74 start-page: 902 year: 2017 end-page: 924 ident: CR5 article-title: A review on time series forecasting techniques for building energy consumption publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2017.02.085 – volume: 24 start-page: 164 issue: 1 year: 2011 end-page: 181 ident: CR22 article-title: A review on time series data mining publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2010.09.007 – volume: 132 start-page: 113 issue: 1 year: 2020 end-page: 130 ident: CR17 article-title: Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China publication-title: Meteorol Atmos Phys doi: 10.1007/s00703-019-00675-8 – year: 2020 ident: CR20 article-title: A deep learning approach for forecasting non-stationary big remote sensing time series publication-title: Arab J Geosci doi: 10.1007/s12517-020-06140-w – year: 2019 ident: 9531_CR18 publication-title: Water doi: 10.3390/w11050977 – ident: 9531_CR29 doi: 10.1609/aaai.v31i1.10735 – volume: 557 start-page: 378 year: 2018 ident: 9531_CR26 publication-title: J Hydrol doi: 10.1016/j.jhydrol.2017.12.044 – volume: 34 start-page: 2425 issue: 3 year: 2022 ident: 9531_CR7 publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06505-6 – volume: 5 start-page: 1260 issue: 4 year: 2012 ident: 9531_CR4 publication-title: Disaster Adv – volume: 74 start-page: 902 year: 2017 ident: 9531_CR5 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2017.02.085 – year: 2020 ident: 9531_CR2 publication-title: Ecol Inform doi: 10.1016/j.ecoinf.2020.101136 – year: 2020 ident: 9531_CR27 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2019.135139 – year: 2022 ident: 9531_CR28 publication-title: Comput Intell Neurosci doi: 10.1155/2022/5596676 – ident: 9531_CR3 doi: 10.1109/ICOTEN52080.2021.9493513 – ident: 9531_CR25 doi: 10.24963/ijcai.2021/225 – volume: 24 start-page: 164 issue: 1 year: 2011 ident: 9531_CR22 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2010.09.007 – ident: 9531_CR6 doi: 10.1109/MWSCAS.2017.8053243 – ident: 9531_CR12 doi: 10.1109/ICMLA.2019.00020 – year: 2020 ident: 9531_CR20 publication-title: Arab J Geosci doi: 10.1007/s12517-020-06140-w – volume: 388 start-page: 269 year: 2020 ident: 9531_CR9 publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.12.118 – volume: 23 start-page: 1696 issue: 7 year: 2010 ident: 9531_CR24 publication-title: J Clim doi: 10.1175/2009JCLI2909.1 – year: 2020 ident: 9531_CR14 publication-title: Arab J Geosci doi: 10.1007/s12517-020-5234-y – volume: 36 start-page: 831 issue: 3 year: 2022 ident: 9531_CR1 publication-title: Stoch Environ Res Risk Assess doi: 10.1007/s00477-021-02078-x – ident: 9531_CR32 – volume: 182 start-page: 72 year: 2019 ident: 9531_CR15 publication-title: Energy doi: 10.1016/j.energy.2019.05.230 – ident: 9531_CR31 doi: 10.1007/s11053-019-09512-6 – year: 2018 ident: 9531_CR8 publication-title: Algorithms doi: 10.3390/a11090132 – volume: 78 start-page: 12686 issue: 10 year: 2022 ident: 9531_CR13 publication-title: J Supercomput doi: 10.1007/s11227-022-04386-7 – volume: 137 start-page: 172 issue: 110 year: 2023 ident: 9531_CR21 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2023.110172 – ident: 9531_CR16 doi: 10.1109/CSAIEE54046.2021.9543210 – year: 2019 ident: 9531_CR19 publication-title: Appl Sci doi: 10.3390/app9071345 – ident: 9531_CR10 doi: 10.1109/YAC.2016.7804912 – volume: 132 start-page: 113 issue: 1 year: 2020 ident: 9531_CR17 publication-title: Meteorol Atmos Phys doi: 10.1007/s00703-019-00675-8 – volume: 72 start-page: 37 issue: 1 year: 2018 ident: 9531_CR23 publication-title: Am Stat doi: 10.1080/00031305.2017.1380080 – year: 2018 ident: 9531_CR11 publication-title: Water doi: 10.3390/w10111543 – volume: 31 start-page: 2471 issue: 10 year: 2017 ident: 9531_CR30 publication-title: Stoch Environ Res Risk Assess doi: 10.1007/s00477-017-1437-5
SSID	ssj0021753
Score	2.4487064
Snippet	Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	6775
SubjectTerms	Accuracy Application of Soft Computing Artificial Intelligence Artificial neural networks Autoregressive models Big Data Climate change Coders Computational Intelligence Control Correlation coefficients Datasets Deep learning Drought Engineering Forecasting Forecasting techniques General circulation models Mathematical Logic and Foundations Mechatronics Multivariate analysis Neural networks Optimization Precipitation Rain Remote sensing Remote sensors Robotics Root-mean-square errors Sensors Support vector machines Time series Variables
SummonAdditionalLinks	– databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEA5aPXixPrFaJQdvGuy-d09SxepBSlErxcuSJxSkrd3as__Bf-gvcSbNtijYi7eFZMOy32Qeycw3hJyGvhQ6SjxmwtBnYRYKxrHYJ_E1Hjl4SWx5tp_vk3Y77fWyjjtwK1xaZakTraJWQ4ln5BcoixCfx5l3OXpj2DUKb1ddC41VsoYsCdi6oRO9zAMux0IJLgF4kWB6XdGMLZ1D4pMGA4vFkHHNY9lPw7TwNn9dkFq706r-94u3yKbzOGlzJiLbZEUPdki17OZA3ebeJY9NetVntw9dhpZNUWS4VHr89fGptH2ipkzkouDpUpuKOIVQG7xVii3qKUqzLnBQS15gPvUe6bZunq7vmGu5wCTsxQnjQZQYIbiJVcQFhDqBlGlgTMClSQLuh8qISAC-ceLpRqphchLzMPK0EZ7hjWCfVAbDgT4gFHQtV34gMm54KFOVZhLUS6wgIOe-rxs14pX_O5eOjxzbYrzmcyZli1EOGOUWozyrkbP5O6MZG8fS2fUSmNztzCJfoFIj5yW0i-G_VztcvtoR2fDB35nlQtZJZTJ-18dkXU4n_WJ8YuXyG0K-6ME priority: 102 providerName: ProQuest
Title	A Bi-GRU-based encoder–decoder framework for multivariate time series forecasting
URI	https://link.springer.com/article/10.1007/s00500-023-09531-9 https://www.proquest.com/docview/3073390691
Volume	28
WOSCitedRecordID	wos001136826400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: AAdvanced Technologies & Aerospace Database (subscription) customDbUrl: eissn: 1433-7479 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0021753 issn: 1432-7643 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1433-7479 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0021753 issn: 1432-7643 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central - New (Subscription) customDbUrl: eissn: 1433-7479 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0021753 issn: 1432-7643 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-7479 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0021753 issn: 1432-7643 databaseCode: RSV dateStart: 19970401 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NSgMxEB60etCD1apYrSUHbxrY_-weW2kVlFJaK-JlSbIJFKRKt_bsO_iGPomT7e5WRQW9LAvJhmWSzHyTzHwDcOI5Uiif2VR7nkO9yBOUm2Qf5ihz5GCzIOPZvr1mvV54dxf186SwtIh2L64kM01dJrsZqhKLoo2hhiPNptEqrKG5C03BhsHwtnSzcu5JBAKIHdHg5qky34_x2RwtMeaXa9HM2nSr__vPbdjK0SVpLZbDDqyoSQ2qReUGkm_kGmx-oCHchWGLtMf0YjCixqYlxHBbJmr69vKaqOyN6CKEiyDGJVkQ4hydbMSpxBSnJ2Ydq9Q0KslTE0m9B6Nu5-b8kubFFqjEXTij3PWZFoLrIPG5QCfHlTJ0tXa51Mzljpdo4Quc2YDZygoVdmYB93xbaWFrbrn7UJk8TtQBENSyPHFcEXHNPRkmYSRRsQQJuuLccZRVB7uQeSxzJnJTEOMhLjmUMxnGKMM4k2Ec1eG0_OZpwcPxa-9GMZVxvifT2GgzN7KCyK7DWTF1y-afRzv8W_cj2HAQ-SyiIhtQmU2f1TGsy_lsnE6bsNbu9PqDJqxeMYrPvn_fzNbvOz0a5ok
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qLRJs-gCqTmmLF2UFFonjxMkCodK3ZhghaFF3qZ_SSDAtk9CqO_6B_-hH9Uu49iQzKhLdddFdJD8kx-c-bN97LsAmZ1rZVMTUcc4oL7ii0if7CGb9lUMsssCz_a0n-v385KT4PAPXbS6MD6tsdWJQ1OZM-zvydx6LeD7PivjD-U_qq0b519W2hMYYFl17dYlHtur94Q7u72vG9naPtg9oU1WAaoRbTWWSCqeUdJlJpUJvPtE6T5xLpHYikYwbp1KFS8hEbKPcYmeRSZ7G1qnYySjBeR_BHE9y4eWqK-jkgNewXqILgl4rmvomSSek6nmilYiihaSe4S2mxW1DOPVu_3mQDXZub-Gh_aFFmG88arI1FoElmLHDZ7DQVqsgjfJ6Dl-3yMcB3f9yTL3lNsQzeBo7uvn9x9jwRVwbqEbQkych1PJCooDWltSDH5Z4abWVb7RaVj5e_AUc38vSlmF2eDa0K0DQlkjDElVIJ7nOTV5oVJ-ZiXgmGbNRB-J2f0vd8K37sh_fywlTdMBEiZgoAybKogNvJmPOx2wjd_Zea4FQNpqnKqco6MDbFkrT5v_Ptnr3bK_gycHRp17ZO-x3X8JThr7dOO5zDWbr0S-7Do_1RT2oRhtBJgic3jfE_gI5jknz
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LTwIxEJ4oGqMHUdSIovbgTRv20X0d8YEaCSEihNumz4TEIIGVs__Bf-gvsV12AY2aGG-btNtsZqadb7Yz3wCcEocz6QU2VoQ4mESEYWqKfQJHml8OduCnPNvdRtBshr1e1Fqo4k-z3fMryWlNg2FpGiTVoVDVWeGboS2xsPY32PCl2ThahhViEulNvN7uzkKujIdSgwKNI7Xzzcpmvl_js2ua480vV6Sp56kX___NW7CZoU5Um5rJNizJQQmKeUcHlG3wEmws0BPuQLuGLvr45qGDja8TyHBeCjl6f30TMn1CKk_tQhr7ojQ5caKDb41fkWlaj4x9y7EZlJyOTYb1LnTq14-XtzhrwoC53p0Jpq4XKMao8oVHmQ5-XM5DVymXchW41CFCMY9pjfuBLa1Q6smBT4lnS8VsRS13DwqD54HcB6RPXyocl0VUUcJDEUZcHzi-0CE6dRxplcHO5R_zjKHcNMp4imfcyqkMYy3DOJVhHJXhbPbOcMrP8evsSq7WONur49iccm5k-ZFdhvNcjfPhn1c7-Nv0E1hrXdXjxl3z_hDWHQ2OpomTFSgkoxd5BKt8kvTHo-PUhD8A_lju5w
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Bi-GRU-based+encoder%E2%80%93decoder+framework+for+multivariate+time+series+forecasting&rft.jtitle=Soft+computing+%28Berlin%2C+Germany%29&rft.au=Balti%2C+Hanen&rft.au=Ben+Abbes%2C+Ali&rft.au=Farah%2C+Imed+Riadh&rft.date=2024-05-01&rft.pub=Springer+Berlin+Heidelberg&rft.issn=1432-7643&rft.eissn=1433-7479&rft.volume=28&rft.issue=9-10&rft.spage=6775&rft.epage=6786&rft_id=info:doi/10.1007%2Fs00500-023-09531-9&rft.externalDocID=10_1007_s00500_023_09531_9
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1432-7643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1432-7643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1432-7643&client=summon