{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:57:44Z","timestamp":1771063064386,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72071001"],"award-info":[{"award-number":["72071001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71871001"],"award-info":[{"award-number":["71871001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2008085MG226"],"award-info":[{"award-number":["2008085MG226"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2108085MG239"],"award-info":[{"award-number":["2108085MG239"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-09106-7","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T17:02:49Z","timestamp":1699894969000},"page":"1313-1328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel hybrid model for freight volume prediction based on the Baidu search index and emergency"],"prefix":"10.1007","volume":"36","author":[{"given":"Jinpei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Na","family":"Chu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-6236","authenticated-orcid":false,"given":"Piao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ligang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Huayou","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"issue":"8","key":"9106_CR1","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1177\/00472875211040569","volume":"61","author":"JW Bi","year":"2022","unstructured":"Bi JW, Li C, Xu H et al (2022) Forecasting daily tourism demand for tourist attractions with big data: an ensemble deep learning method. J Travel Res 61(8):1719\u20131737. https:\/\/doi.org\/10.1177\/00472875211040569","journal-title":"J Travel Res"},{"key":"9106_CR2","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.qref.2016.05.003","volume":"63","author":"M Bourdeau-Brien","year":"2017","unstructured":"Bourdeau-Brien M, Kryzanowski L (2017) The impact of natural disasters on the stock returns and volatilities of local firms. Q Rev Econ Financ 63:259\u2013270. https:\/\/doi.org\/10.1016\/j.qref.2016.05.003","journal-title":"Q Rev Econ Financ"},{"issue":"9","key":"9106_CR3","doi-asserted-by":"publisher","first-page":"2617","DOI":"10.1016\/j.apm.2014.10.065","volume":"39","author":"Y Chen","year":"2015","unstructured":"Chen Y, Yang Y, Liu C et al (2015) A hybrid application algorithm based on the support vector machine and artificial intelligence: an example of electric load forecasting. Appl Math Model 39(9):2617\u20132632. https:\/\/doi.org\/10.1016\/j.apm.2014.10.065","journal-title":"Appl Math Model"},{"issue":"1","key":"9106_CR4","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, McRae JE et al (1990) STL: a seasonal-trend decomposition. J Off Stat 6(1):3\u201373","journal-title":"J Off Stat"},{"key":"9106_CR5","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.eswa.2017.10.053","volume":"96","author":"PP Das","year":"2018","unstructured":"Das PP, Bisoi R, Dash P (2018) Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis. Expert Syst Appl 96:427\u2013449. https:\/\/doi.org\/10.1016\/j.eswa.2017.10.053","journal-title":"Expert Syst Appl"},{"key":"9106_CR6","doi-asserted-by":"publisher","first-page":"112779","DOI":"10.1016\/j.enconman.2020.112779","volume":"212","author":"Y Deng","year":"2020","unstructured":"Deng Y, Wang B, Lu Z (2020) A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting. Energy Convers Manag 212:112779. https:\/\/doi.org\/10.1016\/j.enconman.2020.112779","journal-title":"Energy Convers Manag"},{"key":"9106_CR7","doi-asserted-by":"publisher","first-page":"119397","DOI":"10.1016\/j.energy.2020.119397","volume":"217","author":"J Duan","year":"2021","unstructured":"Duan J, Zuo H, Bai Y et al (2021) Short-term wind speed forecasting using recurrent neural networks with error correction. Energy 217:119397. https:\/\/doi.org\/10.1016\/j.energy.2020.119397","journal-title":"Energy"},{"key":"9106_CR8","doi-asserted-by":"publisher","first-page":"101099","DOI":"10.1016\/j.frl.2019.01.011","volume":"32","author":"J Fang","year":"2020","unstructured":"Fang J, Gozgor G, Lau CKM et al (2020) The impact of Baidu index sentiment on the volatility of China\u2019s stock markets. Financ Res Lett 32:101099. https:\/\/doi.org\/10.1016\/j.frl.2019.01.011","journal-title":"Financ Res Lett"},{"key":"9106_CR9","doi-asserted-by":"publisher","unstructured":"Gao J, Lu X (2015) Forecast of China railway freight volume by random forest regression model. In: 2015 International conference on logistics, informatics and service sciences (LISS), IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/LISS.2015.7369654","DOI":"10.1109\/LISS.2015.7369654"},{"key":"9106_CR10","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.1016\/j.apenergy.2013.03.027","volume":"112","author":"JF Guo","year":"2013","unstructured":"Guo JF, Ji Q (2013) How does market concern derived from the internet affect oil prices? Appl Energy 112:1536\u20131543. https:\/\/doi.org\/10.1016\/j.apenergy.2013.03.027","journal-title":"Appl Energy"},{"issue":"5","key":"9106_CR11","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1515\/jisys-2016-0203","volume":"28","author":"Zd Guo","year":"2017","unstructured":"Guo Zd, Fu JY (2017) Prediction method of railway freight volume based on genetic algorithm improved general regression neural network. J Intell Syst 28(5):835\u2013848. https:\/\/doi.org\/10.1515\/jisys-2016-0203","journal-title":"J Intell Syst"},{"key":"9106_CR12","doi-asserted-by":"publisher","first-page":"101926","DOI":"10.1016\/j.tre.2020.101926","volume":"137","author":"LAH Hassan","year":"2020","unstructured":"Hassan LAH, Mahmassani HS, Chen Y (2020) Reinforcement learning framework for freight demand forecasting to support operational planning decisions. Transp Res Part E Logist Transp Rev 137:101926. https:\/\/doi.org\/10.1016\/j.tre.2020.101926","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"9106_CR13","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.apm.2019.07.001","volume":"76","author":"Z He","year":"2019","unstructured":"He Z, Chen Y, Shang Z et al (2019) A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm. Appl Math Model 76:717\u2013740. https:\/\/doi.org\/10.1016\/j.apm.2019.07.001","journal-title":"Appl Math Model"},{"issue":"5","key":"9106_CR14","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1177\/0047287520921244","volume":"60","author":"W H\u00f6pken","year":"2021","unstructured":"H\u00f6pken W, Eberle T, Fuchs M et al (2021) Improving tourist arrival prediction: a big data and artificial neural network approach. J Travel Res 60(5):998\u20131017. https:\/\/doi.org\/10.1177\/0047287520921244","journal-title":"J Travel Res"},{"issue":"1971","key":"9106_CR15","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):903\u2013995. https:\/\/doi.org\/10.1098\/rspa.1998.0193","journal-title":"Proc R Soc Lond Ser A Math Phys Eng Sci"},{"key":"9106_CR16","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1007\/s10614-019-09886-y","volume":"55","author":"R Huang","year":"2020","unstructured":"Huang R, Nie T, Zhu Y et al (2020) Forecasting trade potential between china and the five central Asian countries: under the background of belt and road initiative. Comput Econ 55:1233\u20131247. https:\/\/doi.org\/10.1007\/s10614-019-09886-y","journal-title":"Comput Econ"},{"key":"9106_CR17","doi-asserted-by":"publisher","first-page":"102679","DOI":"10.1016\/j.scs.2020.102679","volume":"66","author":"EON Jnr","year":"2021","unstructured":"Jnr EON, Ziggah YY, Relvas S (2021) Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting. Sustain Cities Soc 66:102679. https:\/\/doi.org\/10.1016\/j.scs.2020.102679","journal-title":"Sustain Cities Soc"},{"key":"9106_CR18","doi-asserted-by":"publisher","first-page":"106161","DOI":"10.1016\/j.asoc.2020.106161","volume":"90","author":"H Li","year":"2020","unstructured":"Li H, Bai J, Cui X et al (2020) A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting. Appl Soft Comput 90:106161. https:\/\/doi.org\/10.1016\/j.asoc.2020.106161","journal-title":"Appl Soft Comput"},{"key":"9106_CR19","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.apenergy.2019.04.188","volume":"249","author":"H Liu","year":"2019","unstructured":"Liu H, Chen C (2019) Data processing strategies in wind energy forecasting models and applications: a comprehensive review. Appl Energy 249:392\u2013408. https:\/\/doi.org\/10.1016\/j.apenergy.2019.04.188","journal-title":"Appl Energy"},{"issue":"6","key":"9106_CR20","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1080\/13675567.2020.1837760","volume":"25","author":"W Liu","year":"2022","unstructured":"Liu W, Liang Y, Bao X et al (2022) China\u2019s logistics development trends in the post Covid-19 era. Int J Logist Res Appl 25(6):965\u2013976. https:\/\/doi.org\/10.1080\/13675567.2020.1837760","journal-title":"Int J Logist Res Appl"},{"key":"9106_CR21","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1007\/s00170-016-9254-4","volume":"89","author":"C Ma","year":"2017","unstructured":"Ma C, Zhao L, Mei X et al (2017) Thermal error compensation of high-speed spindle system based on a modified bp neural network. Int J Adv Manuf Technol 89:3071\u20133085. https:\/\/doi.org\/10.1007\/s00170-016-9254-4","journal-title":"Int J Adv Manuf Technol"},{"key":"9106_CR22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.enconman.2016.02.013","volume":"114","author":"A Meng","year":"2016","unstructured":"Meng A, Ge J, Yin H et al (2016) Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manag 114:75\u201388. https:\/\/doi.org\/10.1016\/j.enconman.2016.02.013","journal-title":"Energy Convers Manag"},{"key":"9106_CR23","unstructured":"Montana DJ, Davis L, et al (1989) Training feedforward neural networks using genetic algorithms. In: IJCAI, pp 762\u2013767"},{"key":"9106_CR24","doi-asserted-by":"publisher","first-page":"106389","DOI":"10.1016\/j.asoc.2020.106389","volume":"93","author":"D Niu","year":"2020","unstructured":"Niu D, Wang K, Sun L et al (2020) Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: a case study. Appl Soft Comput 93:106389. https:\/\/doi.org\/10.1016\/j.asoc.2020.106389","journal-title":"Appl Soft Comput"},{"key":"9106_CR25","doi-asserted-by":"publisher","first-page":"121145","DOI":"10.1016\/j.energy.2021.121145","volume":"234","author":"D Niu","year":"2021","unstructured":"Niu D, Ji Z, Li W et al (2021) Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization. Energy 234:121145. https:\/\/doi.org\/10.1016\/j.energy.2021.121145","journal-title":"Energy"},{"key":"9106_CR26","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.renene.2019.06.047","volume":"145","author":"T Ouyang","year":"2020","unstructured":"Ouyang T, Huang H, He Y et al (2020) Chaotic wind power time series prediction via switching data-driven modes. Renew Energy 145:270\u2013281. https:\/\/doi.org\/10.1016\/j.renene.2019.06.047","journal-title":"Renew Energy"},{"key":"9106_CR27","doi-asserted-by":"publisher","first-page":"123913","DOI":"10.1016\/j.physa.2019.123913","volume":"549","author":"Y Peng","year":"2020","unstructured":"Peng Y, Xiang W (2020) Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction. Phys A Stat Mech Appl 549:123913. https:\/\/doi.org\/10.1016\/j.physa.2019.123913","journal-title":"Phys A Stat Mech Appl"},{"key":"9106_CR28","doi-asserted-by":"publisher","first-page":"106266","DOI":"10.1016\/j.engappai.2023.106266","volume":"123","author":"Q Qin","year":"2023","unstructured":"Qin Q, Huang Z, Zhou Z et al (2023) Crude oil price forecasting with machine learning and google search data: an accuracy comparison of single-model versus multiple-model. Eng Appl Artif Intell 123:106266. https:\/\/doi.org\/10.1016\/j.engappai.2023.106266","journal-title":"Eng Appl Artif Intell"},{"key":"9106_CR29","doi-asserted-by":"publisher","unstructured":"Qiu YY, Zhang Q, Lei M (2020) Forecasting the railway freight volume in china based on combined pso-lstm model. In: Journal of physics: conference series, IOP Publishing, p 012029. https:\/\/doi.org\/10.1088\/1742-6596\/1651\/1\/012029","DOI":"10.1088\/1742-6596\/1651\/1\/012029"},{"key":"9106_CR30","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1134\/s0005117917010064","volume":"78","author":"KV Rudakov","year":"2017","unstructured":"Rudakov KV, Strizhov VV, Kashirin DO et al (2017) Selecting an optimal model for forecasting the volumes of railway goods transportation. Autom Remote Control 78:75\u201387. https:\/\/doi.org\/10.1134\/s0005117917010064","journal-title":"Autom Remote Control"},{"issue":"7","key":"9106_CR31","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1007\/s00521-020-05141-w","volume":"33","author":"JJ Ruiz-Aguilar","year":"2021","unstructured":"Ruiz-Aguilar JJ, Turias I, Gonz\u00e1lez-Enrique J et al (2021) A permutation entropy-based EMD-ANN forecasting ensemble approach for wind speed prediction. Neural Comput Appl 33(7):2369\u20132391. https:\/\/doi.org\/10.1007\/s00521-020-05141-w","journal-title":"Neural Comput Appl"},{"issue":"10","key":"9106_CR32","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.1080\/1540496X.2020.1785424","volume":"56","author":"AA Salisu","year":"2020","unstructured":"Salisu AA, Akanni LO (2020) Constructing a global fear index for the Covid-19 pandemic. Emerg Mark Financ Trade 56(10):2310\u20132331. https:\/\/doi.org\/10.1080\/1540496X.2020.1785424","journal-title":"Emerg Mark Financ Trade"},{"key":"9106_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/1846926","volume":"2020","author":"D \u0160tifani\u0107","year":"2020","unstructured":"\u0160tifani\u0107 D, Musulin J, Mio\u010devi\u0107 A et al (2020) Impact of Covid-19 on forecasting stock prices: an integration of stationary wavelet transform and bidirectional long short-term memory. Complexity 2020:1\u201312. https:\/\/doi.org\/10.1155\/2020\/1846926","journal-title":"Complexity"},{"key":"9106_CR34","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.apm.2021.03.020","volume":"97","author":"S Sun","year":"2021","unstructured":"Sun S, Jin F, Li H et al (2021) A new hybrid optimization ensemble learning approach for carbon price forecasting. Appl Math Model 97:182\u2013205. https:\/\/doi.org\/10.1016\/j.apm.2021.03.020","journal-title":"Appl Math Model"},{"key":"9106_CR35","doi-asserted-by":"publisher","first-page":"118671","DOI":"10.1016\/j.jclepro.2019.118671","volume":"243","author":"W Sun","year":"2020","unstructured":"Sun W, Huang C (2020) A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network. J Clean Prod 243:118671. https:\/\/doi.org\/10.1016\/j.jclepro.2019.118671","journal-title":"J Clean Prod"},{"key":"9106_CR36","doi-asserted-by":"publisher","first-page":"5323","DOI":"10.1007\/s00500-018-3023-2","volume":"22","author":"J Wang","year":"2018","unstructured":"Wang J, Li X (2018) A combined neural network model for commodity price forecasting with SSA. Soft Comput 22:5323\u20135333. https:\/\/doi.org\/10.1007\/s00500-018-3023-2","journal-title":"Soft Comput"},{"key":"9106_CR37","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.energy.2014.08.064","volume":"76","author":"J Wang","year":"2014","unstructured":"Wang J, Xiong S (2014) A hybrid forecasting model based on outlier detection and fuzzy time series-a case study on Hainan wind farm of china. Energy 76:526\u2013541. https:\/\/doi.org\/10.1016\/j.energy.2014.08.064","journal-title":"Energy"},{"key":"9106_CR38","doi-asserted-by":"publisher","first-page":"152600","DOI":"10.1016\/j.scitotenv.2021.152600","volume":"812","author":"L Wen","year":"2022","unstructured":"Wen L, Song Q (2022) Simulation study on carbon emission of China\u2019s freight system under the target of carbon peaking. Sci Total Environ 812:152600. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.152600","journal-title":"Sci Total Environ"},{"key":"9106_CR39","doi-asserted-by":"publisher","first-page":"120403","DOI":"10.1016\/j.energy.2021.120403","volume":"226","author":"B Wu","year":"2021","unstructured":"Wu B, Wang L, Wang S et al (2021) Forecasting the us oil markets based on social media information during the Covid-19 pandemic. Energy 226:120403. https:\/\/doi.org\/10.1016\/j.energy.2021.120403","journal-title":"Energy"},{"key":"9106_CR40","doi-asserted-by":"publisher","first-page":"104208","DOI":"10.1016\/j.tourman.2020.104208","volume":"82","author":"G Xie","year":"2021","unstructured":"Xie G, Qian Y, Wang S (2021) Forecasting Chinese cruise tourism demand with big data: an optimized machine learning approach. Tour Manag 82:104208. https:\/\/doi.org\/10.1016\/j.tourman.2020.104208","journal-title":"Tour Manag"},{"key":"9106_CR41","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.1007\/s00521-019-04063-6","volume":"32","author":"Q Xu","year":"2020","unstructured":"Xu Q, Wang L, Jiang C et al (2020) A novel (U) MIDAS-SVR model with multi-source market sentiment for forecasting stock returns. Neural Comput Appl 32:5875\u20135888. https:\/\/doi.org\/10.1007\/s00521-019-04063-6","journal-title":"Neural Comput Appl"},{"key":"9106_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6686786","volume":"2021","author":"F Yang","year":"2021","unstructured":"Yang F, Tang X, Gan Y et al (2021) Forecast of freight volume in Xi\u2019an based on gray GM (1, 1) model and Markov forecasting model. J Math 2021:1\u20136. https:\/\/doi.org\/10.1155\/2021\/6686786","journal-title":"J Math"},{"key":"9106_CR43","doi-asserted-by":"publisher","first-page":"104217","DOI":"10.1016\/j.engappai.2021.104217","volume":"101","author":"Y Yang","year":"2021","unstructured":"Yang Y, Guo J, Sun S et al (2021) Forecasting crude oil price with a new hybrid approach and multi-source data. Eng Appl Artif Intell 101:104217. https:\/\/doi.org\/10.1016\/j.engappai.2021.104217","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"9106_CR44","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1109\/TIE.2016.2613974","volume":"64","author":"S Yin","year":"2016","unstructured":"Yin S, Jiang Y, Tian Y et al (2016) A data-driven fuzzy information granulation approach for freight volume forecasting. IEEE Trans Ind Electron 64(2):1447\u20131456. https:\/\/doi.org\/10.1109\/TIE.2016.2613974","journal-title":"IEEE Trans Ind Electron"},{"key":"9106_CR45","doi-asserted-by":"publisher","first-page":"9551","DOI":"10.1109\/ACCESS.2019.2963540","volume":"8","author":"N Yu","year":"2020","unstructured":"Yu N, Xu W, Yu KL (2020) Research on regional logistics demand forecast based on improved support vector machine: a case study of Qingdao city under the new free trade zone strategy. IEEE Access 8:9551\u20139564. https:\/\/doi.org\/10.1109\/ACCESS.2019.2963540","journal-title":"IEEE Access"},{"key":"9106_CR46","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2015.09.001","volume":"37","author":"Z Yu","year":"2015","unstructured":"Yu Z, Yang C, Zhang Z et al (2015) Error correction method based on data transformational GM (1, 1) and application on tax forecasting. Appl Soft Comput 37:554\u2013560. https:\/\/doi.org\/10.1016\/j.asoc.2015.09.001","journal-title":"Appl Soft Comput"},{"issue":"1","key":"9106_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40854-020-00216-y","volume":"7","author":"W Zhang","year":"2021","unstructured":"Zhang W, Yan K, Shen D (2021) Can the Baidu index predict realized volatility in the Chinese stock market? Financ Innov 7(1):1\u201331. https:\/\/doi.org\/10.1186\/s40854-020-00216-y","journal-title":"Financ Innov"},{"key":"9106_CR48","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.asoc.2018.01.017","volume":"65","author":"X Zhang","year":"2018","unstructured":"Zhang X, Wang J (2018) A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl Soft Comput 65:478\u2013494. https:\/\/doi.org\/10.1016\/j.asoc.2018.01.017","journal-title":"Appl Soft Comput"},{"key":"9106_CR49","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1016\/j.apenergy.2019.05.016","volume":"250","author":"Q Zhou","year":"2019","unstructured":"Zhou Q, Wang C, Zhang G (2019) Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems. Appl Energy 250:1559\u20131580. https:\/\/doi.org\/10.1016\/j.apenergy.2019.05.016","journal-title":"Appl Energy"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09106-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09106-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09106-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T08:12:43Z","timestamp":1704442363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09106-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["9106"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09106-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"16 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}