{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:46:47Z","timestamp":1761976007633,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11063-022-10986-4","type":"journal-article","created":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T20:02:35Z","timestamp":1661112155000},"page":"2825-2841","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Support Vector Based Hybrid Forecasting Model for Chaotic Time Series: Spare Part Consumption Prediction"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8300-7288","authenticated-orcid":false,"given":"Saba","family":"Sareminia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"10986_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2018.04.015","author":"J Dombi","year":"2018","unstructured":"Dombi J, J\u00f3n\u00e1s T, Eszter T\u00f3th Z (2018) Modeling and long-term forecasting demand in spare parts logistics businesses. Int J Prod Econ. https:\/\/doi.org\/10.1016\/j.ijpe.2018.04.015","journal-title":"Int J Prod Econ"},{"issue":"2","key":"10986_CR2","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1016\/j.amc.2006.01.064","volume":"181","author":"Z Hua","year":"2006","unstructured":"Hua Z, Zhang B (2006) A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl Math Comput 181(2):1035\u20131048. https:\/\/doi.org\/10.1016\/j.amc.2006.01.064","journal-title":"Appl Math Comput"},{"key":"10986_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2017.05.002","author":"F Guo","year":"2017","unstructured":"Guo F, Diao J, Zhao Q, Wang D, Sun Q (2017) A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. Comput Ind Eng. https:\/\/doi.org\/10.1016\/j.cie.2017.05.002","journal-title":"Comput Ind Eng"},{"issue":"1\u20132","key":"10986_CR4","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/s10618-010-0193-7","volume":"22","author":"A McGovern","year":"2011","unstructured":"McGovern A, Rosendahl D, Brown R (2011) Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min Knowl Disc 22(1\u20132):232\u2013258. https:\/\/doi.org\/10.1007\/s10618-010-0193-7","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"10986_CR5","doi-asserted-by":"publisher","first-page":"926","DOI":"10.3390\/e15030926","volume":"15","author":"M Bozic","year":"2013","unstructured":"Bozic M, Stojanovic M, Stajic Z (2013) Mutual information-based inputs selection for electric load time series forecasting. Entropy 15(3):926\u2013942. https:\/\/doi.org\/10.3390\/e15030926","journal-title":"Entropy"},{"key":"10986_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2020.04.005","author":"V Lukinskiy","year":"2020","unstructured":"Lukinskiy V, Lukinskiy V, Sokolov B (2020) Control of inventory dynamics: A survey of special cases for products with low demand. Ann Rev Cont. https:\/\/doi.org\/10.1016\/j.arcontrol.2020.04.005","journal-title":"Ann Rev Cont"},{"issue":"3","key":"10986_CR7","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s00500-013-1070-2","volume":"18","author":"H Niu","year":"2014","unstructured":"Niu H, Wang J (2014) Financial time series prediction by a random data-time ef- fective RBF neural network. Soft Comput 18(3):497\u2013508. https:\/\/doi.org\/10.1007\/s00500-013-1070-2","journal-title":"Soft Comput"},{"key":"10986_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2019.109412","volume":"130","author":"ZD Tian","year":"2020","unstructured":"Tian ZD (2020) Chaotic characteristic analysis of network traffic time series at different time scales. Chaos, Solitons Fractals 130:109412. https:\/\/doi.org\/10.1016\/j.chaos.2019.109412","journal-title":"Chaos, Solitons Fractals"},{"issue":"1","key":"10986_CR9","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2012.01.014","volume":"86","author":"R Chandra","year":"2012","unstructured":"Chandra R, Zhang M (2012) Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86(1):116\u2013123. https:\/\/doi.org\/10.1016\/j.neucom.2012.01.014","journal-title":"Neurocomputing"},{"issue":"5","key":"10986_CR10","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2014.05.068","volume":"145","author":"X Wang","year":"2014","unstructured":"Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145(5):90\u201397. https:\/\/doi.org\/10.1016\/j.neucom.2014.05.068","journal-title":"Neurocomputing"},{"key":"10986_CR11","doi-asserted-by":"publisher","DOI":"10.7498\/aps.63.160508","author":"Z Tian","year":"2014","unstructured":"Tian Z, Gao X, Shi T (2014) Combination kernel function least-squares support vector machine for chaotic time series prediction. Acta Physica Sinica. https:\/\/doi.org\/10.7498\/aps.63.160508","journal-title":"Acta Physica Sinica"},{"key":"10986_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110366","author":"LH Tang","year":"2020","unstructured":"Tang LH, Bai YL, Yang J, Lu YN (2020) A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series. Chaos, Solit, Fract. https:\/\/doi.org\/10.1016\/j.chaos.2020.110366","journal-title":"Chaos, Solit, Fract"},{"issue":"2","key":"10986_CR13","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","volume":"11","author":"M Khashei","year":"2011","unstructured":"Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664\u20132675. https:\/\/doi.org\/10.1016\/j.asoc.2010.10.015","journal-title":"Appl Soft Comput"},{"key":"10986_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/en13133332","author":"Y Bai","year":"2020","unstructured":"Bai Y, Tang L, Fan M, Ma X, Yang Y (2020) Fuzzy first-order transition-rules-trained hybrid forecasting system for short-term wind speed forecasts. Energies. https:\/\/doi.org\/10.3390\/en13133332","journal-title":"Energies"},{"key":"10986_CR15","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1155\/2010\/720190","volume":"2010","author":"Z Liu","year":"2010","unstructured":"Liu Z (2010) Chaotic time series analysis. Math Probl Eng 2010:31. https:\/\/doi.org\/10.1155\/2010\/720190","journal-title":"Math Probl Eng"},{"key":"10986_CR16","volume-title":"Exploring chaos: theory and experiment","author":"B Davies","year":"2005","unstructured":"Davies B (2005) Exploring chaos: theory and experiment. Perseus Books, New York"},{"issue":"12","key":"10986_CR17","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","volume":"26","author":"R Chandra","year":"2015","unstructured":"Chandra R (2015) Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time series prediction. IEEE Trans Neur Netw Learn Sys 26(12):3123\u20133136. https:\/\/doi.org\/10.1109\/TNNLS.2015.2404823","journal-title":"IEEE Trans Neur Netw Learn Sys"},{"issue":"8","key":"10986_CR18","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1142\/S0129183100001474","volume":"11","author":"M Osaka","year":"2000","unstructured":"Osaka M (2000) Local box-counting to determine the fractal dimension of high-order chaos. Int J Mod Phys 11(8):1519\u20131526","journal-title":"Int J Mod Phys"},{"issue":"4300","key":"10986_CR19","first-page":"287","volume":"197","author":"MC Mackey","year":"1977","unstructured":"Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Sci Comput Program 197(4300):287\u2013289","journal-title":"Sci Comput Program"},{"issue":"3","key":"10986_CR20","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1016\/j.eswa.2012.05.040","volume":"40","author":"E Egrioglu","year":"2013","unstructured":"Egrioglu E, Aladag CH, Yolcu U (2013) Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst Appl 40(3):854\u2013857. https:\/\/doi.org\/10.1016\/j.eswa.2012.05.040","journal-title":"Expert Syst Appl"},{"issue":"27","key":"10986_CR21","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s00521-015-1903-2","volume":"16","author":"W Guo","year":"2016","unstructured":"Guo W, Xu T, Lu Z (2016) An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution. Neural Comput Appl 16(27):883\u2013898. https:\/\/doi.org\/10.1007\/s00521-015-1903-2","journal-title":"Neural Comput Appl"},{"key":"10986_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2017.03.018","author":"ZD Tian","year":"2014","unstructured":"Tian ZD, Li SJ, Wang YH, Sha Y (2014) A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos, Solitons Fract. https:\/\/doi.org\/10.1016\/j.chaos.2017.03.018","journal-title":"Chaos, Solitons Fract"},{"key":"10986_CR23","doi-asserted-by":"crossref","unstructured":"Ganjefar S, Tofighi M (2018) \"Optimization of quantum-inspired neural network using a memetic algorithm for function approximation and chaotic time series prediction,\" Neurocomputing, vol. 291, no. 1, pp. 175\u2013186, 2018, https:\/\/www.onacademic.com\/detail\/journal_1000040228116710_9b0d.html.","DOI":"10.1016\/j.neucom.2018.02.074"},{"key":"10986_CR24","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001420510088","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Kalra G, Jun T (2019) Chaotic time series prediction using a novel Echo State Network model with inputs reconstruction, Bayesian ridge regression, and independent component analysis. Int J Patt Recogn Artif Intell. https:\/\/doi.org\/10.1142\/S0218001420510088","journal-title":"Int J Patt Recogn Artif Intell"},{"key":"10986_CR25","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.eswa.2018.11.019","volume":"120","author":"H-F Yang","year":"2019","unstructured":"Yang H-F, Phoebe Chen Y-P (2019) Hybrid deep learning and empirical mode decomposition model for time series applications. Expert Sys Appl 120:128\u2013138","journal-title":"Expert Sys Appl"},{"key":"10986_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s40314-019-1006-2","author":"R Wang","year":"2020","unstructured":"Wang R, Peng C, Gao J (2020) A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series. Comput Appl Math. https:\/\/doi.org\/10.1007\/s40314-019-1006-2","journal-title":"Comput Appl Math"},{"issue":"2","key":"10986_CR27","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1002\/we.2422","volume":"23","author":"Z Tian","year":"2020","unstructured":"Tian Z, Li S, Wang Y (2020) A prediction approach using ensemble empirical mode decomposition-permutation entropy and regularized extreme learn- ing machine for short-term wind speed. Wind Energy 23(2):177\u2013206. https:\/\/doi.org\/10.1002\/we.2422","journal-title":"Wind Energy"},{"key":"10986_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103573","author":"ZD Tian","year":"2020","unstructured":"Tian ZD (2020) Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM. Eng Appl Artif Intell. https:\/\/doi.org\/10.1016\/j.engappai.2020.103573","journal-title":"Eng Appl Artif Intell"},{"issue":"6","key":"10986_CR29","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1177\/0142331219885273","volume":"42","author":"ZD Tian","year":"2020","unstructured":"Tian ZD (2020) Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump. Trans Inst Meas Control 42(6):1135\u20131150. https:\/\/doi.org\/10.1177\/0142331219885273","journal-title":"Trans Inst Meas Control"},{"key":"10986_CR30","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2020.1842936","author":"P Jiang","year":"2021","unstructured":"Jiang P, Huang Y, Liu X (2021) Intermittent demand forecasting for spare parts in the heavy-Duty Vehicle Industry: a support vector machine model. Int J Prod Resea. https:\/\/doi.org\/10.1080\/00207543.2020.1842936","journal-title":"Int J Prod Resea"},{"key":"10986_CR31","doi-asserted-by":"publisher","unstructured":"Boukhtouta A, Jentsch P (2018) \"Support vector machine for demand forecasting of Canadian armed forces spare parts,\" In: 6th International symposium on computational and business intelligence (ISCBI), Basel, Switzerland, 2018: IEEE, pp. 59\u201364, DOI: https:\/\/doi.org\/10.1109\/ISCBI.2018.00021","DOI":"10.1109\/ISCBI.2018.00021"},{"key":"10986_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115545","author":"KV Gajendra","year":"2021","unstructured":"Gajendra KV, Chinmoy P, Elsawah AM (2021) A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series. Exp Sys Appl. https:\/\/doi.org\/10.1016\/j.eswa.2021.115545","journal-title":"Exp Sys Appl"},{"issue":"1","key":"10986_CR33","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, MacRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3\u201373","journal-title":"J Off Stat"},{"key":"10986_CR34","volume-title":"Deep learning (adaptive computation and machine learning series)","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning (adaptive computation and machine learning series). The MIT Press, New York University"},{"key":"10986_CR35","first-page":"1172","volume":"143","author":"JZ Wang","year":"2019","unstructured":"Wang JZ, Wang SQ, Yang WD (2019) A novel non-linear combination system for short-term wind speed forecast. Energy Built Environ 143:1172\u20131192","journal-title":"Energy Built Environ"},{"key":"10986_CR36","unstructured":"Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd ed. Morgan Kaufmann"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10986-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10986-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10986-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T12:12:55Z","timestamp":1688818375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10986-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10986"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10986-4","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,8,21]]},"assertion":[{"value":"22 July 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"I hereby accept the terms of your Journal\u2019s ethical codes. The author did not collect any personal information.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}}]}}