{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T01:09:13Z","timestamp":1779239353348,"version":"3.51.4"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-44461-0","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:48:33Z","timestamp":1775112513000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An effective AI infused demand forecasting application for automotive spare parts industry: a real case from Turkey"],"prefix":"10.1038","volume":"16","author":[{"given":"\u00d6zge","family":"Albayrak \u00dcnal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Burak","family":"Erkayman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bilal","family":"Usanmaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"issue":"3","key":"44461_CR1","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1016\/j.ejor.2022.10.006","volume":"307","author":"R Sarlo","year":"2023","unstructured":"Sarlo, R., Fernandes, C. & Borenstein, D. Lumpy and intermittent retail demand forecasts with score-driven models. Eur. J. Oper. Res. 307 (3), 1146\u20131160 (2023).","journal-title":"Eur. J. Oper. Res."},{"issue":"2","key":"44461_CR2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1080\/13675567.2020.1803246","volume":"25","author":"J Feizabadi","year":"2022","unstructured":"Feizabadi, J. Machine learning demand forecasting and supply chain performance. Int. J. Logistics Res. Appl. 25 (2), 119\u2013142 (2022).","journal-title":"Int. J. Logistics Res. Appl."},{"issue":"4","key":"44461_CR3","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1287\/msom.1050.0087","volume":"7","author":"D Simchi-Levi","year":"2005","unstructured":"Simchi-Levi, D. & Zhao, Y. Safety stock positioning in supply chains with stochastic lead times. Manuf. Service Oper. Manage. 7 (4), 295\u2013318 (2005).","journal-title":"Manuf. Service Oper. Manage."},{"issue":"4","key":"44461_CR4","first-page":"2605","volume":"30","author":"\u00d6 Albayrak \u00dcnal","year":"2023","unstructured":"Albayrak \u00dcnal, \u00d6., Erkayman, B. & Usanmaz, B. Applications of artificial intelligence in inventory management: A systematic review of the literature. Arch. Comput. Methods Eng. 30 (4), 2605\u20132625 (2023).","journal-title":"Arch. Comput. Methods Eng."},{"key":"44461_CR5","doi-asserted-by":"crossref","unstructured":"Yuna, F. & Erkayman, B. A genetic algorithm-based model for inventory control in intermittent demands. Avrupa Bilim ve Teknoloji Dergisi (32), 696\u2013701. (2021).","DOI":"10.31590\/ejosat.1039251"},{"key":"44461_CR6","doi-asserted-by":"crossref","unstructured":"Kiefer, D. et al. Demand forecasting intermittent and lumpy time series: Comparing statistical, machine learning and deep learning methods. (2021).","DOI":"10.24251\/HICSS.2021.172"},{"key":"44461_CR7","doi-asserted-by":"crossref","unstructured":"Muhaimin, A., Prastyo, D. D. & Lu, H. H. S. Forecasting with recurrent neural network in intermittent demand data. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). (IEEE, 2021).","DOI":"10.1109\/Confluence51648.2021.9376880"},{"issue":"6","key":"44461_CR8","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1057\/jors.2014.62","volume":"66","author":"F Petropoulos","year":"2015","unstructured":"Petropoulos, F. & Kourentzes, N. Forecast combinations for intermittent demand. J. Oper. Res. Soc. 66 (6), 914\u2013924 (2015).","journal-title":"J. Oper. Res. Soc."},{"issue":"10","key":"44461_CR9","doi-asserted-by":"publisher","first-page":"6487","DOI":"10.1007\/s00500-023-07871-0","volume":"27","author":"F Yuna","year":"2023","unstructured":"Yuna, F., Erkayman, B. & Y\u0131lmaz, M. Inventory control model for intermittent demand: a comparison of metaheuristics. Soft. Comput. 27 (10), 6487\u20136505 (2023).","journal-title":"Soft. Comput."},{"issue":"2","key":"44461_CR10","first-page":"99","volume":"31","author":"\u0130 G\u00fcven","year":"2021","unstructured":"G\u00fcven, \u0130., Uygun, \u00d6. & \u015eim\u015fir, F. Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors. Text. Appar. 31 (2), 99\u2013110 (2021).","journal-title":"Text. Appar."},{"issue":"2","key":"44461_CR11","first-page":"1035","volume":"181","author":"Z Hua","year":"2006","unstructured":"Hua, Z. & Zhang, B. A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl. Math. Comput. 181 (2), 1035\u20131048 (2006).","journal-title":"Appl. Math. Comput."},{"issue":"2","key":"44461_CR12","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.ijpe.2007.01.007","volume":"111","author":"RS Gutierrez","year":"2008","unstructured":"Gutierrez, R. S., Solis, A. O. & Mukhopadhyay, S. Lumpy demand forecasting using neural networks. Int. J. Prod. Econ. 111 (2), 409\u2013420 (2008).","journal-title":"Int. J. Prod. Econ."},{"issue":"1","key":"44461_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s44163-023-00085-1","volume":"3","author":"Y Ahmadov","year":"2023","unstructured":"Ahmadov, Y. & Helo, P. Deep learning-based approach for forecasting intermittent online sales. Discover Artif. Intell. 3 (1), 45 (2023).","journal-title":"Discover Artif. Intell."},{"key":"44461_CR14","doi-asserted-by":"publisher","first-page":"107038","DOI":"10.1016\/j.asoc.2020.107038","volume":"101","author":"S Cui","year":"2021","unstructured":"Cui, S. et al. A stacking-based ensemble learning method for earthquake casualty prediction. Appl. Soft Comput. 101, 107038 (2021).","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"44461_CR15","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.jrmge.2020.05.011","volume":"13","author":"N Kardani","year":"2021","unstructured":"Kardani, N. et al. Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J. Rock Mech. Geotech. Eng. 13 (1), 188\u2013201 (2021).","journal-title":"J. Rock Mech. Geotech. Eng."},{"issue":"1","key":"44461_CR16","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.ijpe.2013.01.009","volume":"143","author":"N Kourentzes","year":"2013","unstructured":"Kourentzes, N. Intermittent demand forecasts with neural networks. Int. J. Prod. Econ. 143 (1), 198\u2013206 (2013).","journal-title":"Int. J. Prod. Econ."},{"key":"44461_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.14257\/ijca.2018.11.3.14","volume":"11","author":"K Lee","year":"2018","unstructured":"Lee, K. et al. Intermittent demand forecasting with a recurrent neural network model using IoT data. Int. J. Control Autom. 11, 153\u2013168 (2018).","journal-title":"Int. J. Control Autom."},{"key":"44461_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ijpe.2018.01.026","volume":"209","author":"MZ Babai","year":"2019","unstructured":"Babai, M. Z. et al. A new method to forecast intermittent demand in the presence of inventory obsolescence. Int. J. Prod. Econ. 209, 30\u201341 (2019).","journal-title":"Int. J. Prod. Econ."},{"issue":"17","key":"44461_CR19","doi-asserted-by":"publisher","first-page":"26145","DOI":"10.1007\/s11042-021-10913-0","volume":"80","author":"KK Chandriah","year":"2021","unstructured":"Chandriah, K. K. & Naraganahalli, R. V. RNN\/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools Appl. 80 (17), 26145\u201326159 (2021).","journal-title":"Multimedia Tools Appl."},{"key":"44461_CR20","doi-asserted-by":"publisher","first-page":"102662","DOI":"10.1016\/j.jretconser.2021.102662","volume":"62","author":"X Tian","year":"2021","unstructured":"Tian, X., Wang, H. & Erjiang, E. Forecasting intermittent demand for inventory management by retailers: A new approach. J. Retailing Consumer Serv. 62, 102662 (2021).","journal-title":"J. Retailing Consumer Serv."},{"issue":"2","key":"44461_CR21","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.dsm.2022.04.001","volume":"5","author":"X Zhuang","year":"2022","unstructured":"Zhuang, X., Yu, Y. & Chen, A. A combined forecasting method for intermittent demand using the automotive aftermarket data. Data Sci. Manage. 5 (2), 43\u201356 (2022).","journal-title":"Data Sci. Manage."},{"key":"44461_CR22","doi-asserted-by":"crossref","unstructured":"Wang, S., Kang, Y. & Petropoulos, F. Combining probabilistic forecasts of intermittent demand. Eur. J. Operationa.Res. (2024).","DOI":"10.1016\/j.ejor.2024.01.032"},{"key":"44461_CR23","doi-asserted-by":"publisher","first-page":"103069","DOI":"10.1016\/j.aei.2024.103069","volume":"64","author":"X Song","year":"2025","unstructured":"Song, X. et al. An aggregate\u2013disaggregate framework for forecasting intermittent demand in fast fashion retailing. Adv. Eng. Inform. 64, 103069 (2025).","journal-title":"Adv. Eng. Inform."},{"key":"44461_CR24","doi-asserted-by":"crossref","unstructured":"Kenaka, S. P. et al. Enhancing intermittent spare part demand forecasting: a novel ensemble approach with focal loss and SMOTE. Logistics 9 (1), 25. (2025).","DOI":"10.3390\/logistics9010025"},{"issue":"6","key":"44461_CR25","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.jairtraman.2005.06.003","volume":"11","author":"A Regattieri","year":"2005","unstructured":"Regattieri, A. et al. Managing lumpy demand for aircraft spare parts. J. Air Transp. Manage. 11 (6), 426\u2013431 (2005).","journal-title":"J. Air Transp. Manage."},{"key":"44461_CR26","doi-asserted-by":"crossref","unstructured":"Amin-Naseri, M. R. & Tabar, B. R. Neural network approach to lumpy demand forecasting for spare parts in process industries. In International Conference on Computer and Communication Engineering (IEEE, 2008).","DOI":"10.1109\/ICCCE.2008.4580831"},{"issue":"8","key":"44461_CR27","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1002\/for.1242","volume":"31","author":"S Mukhopadhyay","year":"2012","unstructured":"Mukhopadhyay, S., Solis, A. O. & Gutierrez, R. S. The accuracy of non-traditional versus traditional methods of forecasting lumpy demand. J. Forecast. 31 (8), 721\u2013735 (2012).","journal-title":"J. Forecast."},{"key":"44461_CR28","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.jairtraman.2015.09.004","volume":"50","author":"III Lowas","year":"2016","unstructured":"Lowas, I. I. I., Ciarallo, F. W. & A.F. and Reliability and operations: Keys to lumpy aircraft spare parts demands. J. Air Transp. Manage. 50, 30\u201340 (2016).","journal-title":"J. Air Transp. Manage."},{"key":"44461_CR29","doi-asserted-by":"crossref","unstructured":"Kalaya, P., Termsuksawad, P. & Wasusri, T. Forecasting lumpy demand for planning inventory: The case of community hospitals in Thailand. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). (IEEE, 2019).","DOI":"10.1109\/IEEM44572.2019.8978541"},{"key":"44461_CR30","unstructured":"Thaiudomsap, N. & Sribundit, N. Study on demand forecasting techniques for drugs with sporadic demand in a community hospital. Sci. Eng. Health Stud. 22050005\u201322050005. (2022)."},{"issue":"9","key":"44461_CR31","doi-asserted-by":"publisher","first-page":"5475","DOI":"10.3390\/app13095475","volume":"13","author":"I Shafi","year":"2023","unstructured":"Shafi, I. et al. Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety. Appl. Sci. 13 (9), 5475 (2023).","journal-title":"Appl. Sci."},{"key":"44461_CR32","doi-asserted-by":"crossref","unstructured":"Martin, D., Spitzer, P. & K\u00fchl, N. A new metric for lumpy and intermittent demand forecasts: Stock-keeping-oriented prediction error costs. https:\/\/arXiv.org\/abs\/2004.10537. (2020).","DOI":"10.24251\/HICSS.2020.121"},{"issue":"15","key":"44461_CR33","doi-asserted-by":"publisher","first-page":"9295","DOI":"10.3390\/su14159295","volume":"14","author":"JM Ro\u017eanec","year":"2022","unstructured":"Ro\u017eanec, J. M., Fortuna, B. & Mladeni\u0107, D. Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand. Sustainability 14 (15), 9295 (2022).","journal-title":"Sustainability"},{"key":"44461_CR34","doi-asserted-by":"publisher","first-page":"109670","DOI":"10.1016\/j.cie.2023.109670","volume":"185","author":"CF Chien","year":"2023","unstructured":"Chien, C. F., Ku, C. C. & Lu, Y. Y. Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study. Comput. Ind. Eng. 185, 109670 (2023).","journal-title":"Comput. Ind. Eng."},{"key":"44461_CR35","doi-asserted-by":"crossref","unstructured":"Affonso, T. B. et al. A new hybrid forecasting method for spare part inventory management using heuristics and bootstrapping. Decis. Analytics J. 100415. (2024).","DOI":"10.1016\/j.dajour.2024.100415"},{"issue":"2","key":"44461_CR36","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1080\/01605682.2024.2349734","volume":"76","author":"M Caserta","year":"2025","unstructured":"Caserta, M. & D\u2019Angelo, L. Intermittent demand forecasting for spare parts with little historical information. J. Oper. Res. Soc. 76 (2), 294\u2013309 (2025).","journal-title":"J. Oper. Res. Soc."},{"key":"44461_CR37","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1057\/palgrave.jors.2601841","volume":"56","author":"AA Syntetos","year":"2005","unstructured":"Syntetos, A. A., Boylan, J. E. & Croston, J. On the categorization of demand patterns. J. Oper. Res. Soc. 56, 495\u2013503 (2005).","journal-title":"J. Oper. Res. Soc."},{"key":"44461_CR38","unstructured":"Syntetos, A. Forecasting of Intermittent Demand (Brunel University Uxbridge, 2001)."},{"issue":"3","key":"44461_CR39","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1057\/jors.1972.50","volume":"23","author":"JD Croston","year":"1972","unstructured":"Croston, J. D. Forecasting and stock control for intermittent demands. J. Oper. Res. Soc. 23 (3), 289\u2013303 (1972).","journal-title":"J. Oper. Res. Soc."},{"key":"44461_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, G. P., Xia, Y. & Xie, M. Intermittent demand forecasting with transformer neural networks. Ann. Oper. Res. 1\u201322. (2023).","DOI":"10.1007\/s10479-023-05447-7"},{"issue":"1\u20133","key":"44461_CR41","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0925-5273(00)00143-2","volume":"71","author":"AA Syntetos","year":"2001","unstructured":"Syntetos, A. A. & Boylan, J. E. On the bias of intermittent demand estimates. Int. J. Prod. Econ. 71 (1\u20133), 457\u2013466 (2001).","journal-title":"Int. J. Prod. Econ."},{"issue":"3","key":"44461_CR42","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.ejor.2011.05.018","volume":"214","author":"RH Teunter","year":"2011","unstructured":"Teunter, R. H., Syntetos, A. A. & Babai, M. Z. Intermittent demand: Linking forecasting to inventory obsolescence. Eur. J. Oper. Res. 214 (3), 606\u2013615 (2011).","journal-title":"Eur. J. Oper. Res."},{"key":"44461_CR43","doi-asserted-by":"publisher","first-page":"109013","DOI":"10.1016\/j.ijpe.2023.109013","volume":"265","author":"I Svetunkov","year":"2023","unstructured":"Svetunkov, I. & Boylan, J. E. iETS: State space model for intermittent demand forecasting. Int. J. Prod. Econ. 265, 109013 (2023).","journal-title":"Int. J. Prod. Econ."},{"key":"44461_CR44","unstructured":"Albayrak \u00dcnal, \u00d6. & Erkayman, B. Multi-criteria inventory classification with machine learning algorithms in the manufacturing industry. In International Conference on Optimization and Data Science in Industrial Engineering. (Springer, 2023)."},{"key":"44461_CR45","doi-asserted-by":"crossref","unstructured":"Vapnik, V. The Nature of Statistical Learning Theory (Springer, 1995).","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"44461_CR46","unstructured":"Drucker, H. et al. Support vector regression machines. Adv. Neural. Inf. Process. Syst. 9. (1996)."},{"key":"44461_CR47","doi-asserted-by":"crossref","unstructured":"K\u0131l\u0131\u00e7, R., Albayrak, \u00d6., \u00dcnal & Erkayman, B. Fleet type planning for private airline transportation after Covid-19. Gazi \u00dcniversitesi Fen Bilimleri Dergisi Part C Tasar\u0131m ve Teknoloji 11 (2). (2023).","DOI":"10.29109\/gujsc.1178375"},{"key":"44461_CR48","doi-asserted-by":"publisher","first-page":"108073","DOI":"10.1016\/j.ijepes.2022.108073","volume":"139","author":"GF Fan","year":"2022","unstructured":"Fan, G. F. et al. Applications of random forest in multivariable response surface for short-term load forecasting. Int. J. Electr. Power Energy Syst. 139, 108073 (2022).","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"1","key":"44461_CR49","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-020-00329-2","volume":"7","author":"M Seyedan","year":"2020","unstructured":"Seyedan, M. & Mafakheri, F. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J. Big Data. 7 (1), 53 (2020).","journal-title":"J. Big Data"},{"issue":"10","key":"44461_CR50","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.3390\/en10101547","volume":"10","author":"K Gajowniczek","year":"2017","unstructured":"Gajowniczek, K. & Z\u0105bkowski, T. Two-stage electricity demand modeling using machine learning algorithms. Energies 10 (10), 1547 (2017).","journal-title":"Energies"},{"issue":"2194","key":"44461_CR51","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim, B. & Zohren, S. Time-series forecasting with deep learning: a survey. Philosophical Trans. Royal Soc. A. 379 (2194), 20200209 (2021).","journal-title":"Philosophical Trans. Royal Soc. A"},{"issue":"1","key":"44461_CR52","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1007\/s10479-023-05447-7","volume":"339","author":"GP Zhang","year":"2024","unstructured":"Zhang, G. P., Xia, Y. & Xie, M. Intermittent demand forecasting with transformer neural networks. Ann. Oper. Res. 339 (1), 1051\u20131072 (2024).","journal-title":"Ann. Oper. Res."},{"key":"44461_CR53","doi-asserted-by":"crossref","unstructured":"Giannopoulos, P. G. et al. Machine learning algorithms in intermittent demand forecasting: a review. Int. J. Prod. Res. 1\u201343. (2025).","DOI":"10.1080\/00207543.2025.2578701"},{"issue":"3","key":"44461_CR54","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.3390\/s23031467","volume":"23","author":"JM Aguiar-P\u00e9rez","year":"2023","unstructured":"Aguiar-P\u00e9rez, J. M. & P\u00e9rez-Ju\u00e1rez, M. \u00c1. An insight of deep learning based demand forecasting in smart grids. Sensors 23 (3), 1467 (2023).","journal-title":"Sensors"},{"issue":"1","key":"44461_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer, I. A. & Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods. 43 (1), 3\u201331 (2000).","journal-title":"J. Microbiol. Methods"},{"issue":"5\u20136","key":"44461_CR56","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","volume":"2","author":"F Murtagh","year":"1991","unstructured":"Murtagh, F. Multilayer perceptrons for classification and regression. Neurocomputing 2 (5\u20136), 183\u2013197 (1991).","journal-title":"Neurocomputing"},{"key":"44461_CR57","unstructured":"Dos Santos, C. & Gatti, M. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers. (2014)."},{"issue":"2","key":"44461_CR58","first-page":"1","volume":"2","author":"\u0130A Metin","year":"2019","unstructured":"Metin, \u0130. A. & Karasulu, B. \u0130nsan aktivitelerinin s\u0131n\u0131fland\u0131r\u0131lmas\u0131nda tekrarlayan sinir a\u011f\u0131 kullanan derin \u00f6\u011frenme tabanl\u0131 yakla\u015f\u0131m. Veri Bilimi. 2 (2), 1\u201310 (2019).","journal-title":"Veri Bilimi"},{"key":"44461_CR59","doi-asserted-by":"publisher","first-page":"124889","DOI":"10.1016\/j.energy.2022.124889","volume":"260","author":"W Zha","year":"2022","unstructured":"Zha, W. et al. Forecasting monthly gas field production based on the CNN-LSTM model. Energy 260, 124889 (2022).","journal-title":"Energy"},{"key":"44461_CR60","doi-asserted-by":"publisher","first-page":"120346","DOI":"10.1016\/j.eswa.2023.120346","volume":"227","author":"B G\u00fclmez","year":"2023","unstructured":"G\u00fclmez, B. Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Syst. Appl. 227, 120346 (2023).","journal-title":"Expert Syst. Appl."},{"key":"44461_CR61","doi-asserted-by":"publisher","first-page":"109019","DOI":"10.1016\/j.jneumeth.2020.109019","volume":"350","author":"Y Yang","year":"2021","unstructured":"Yang, Y. et al. Classification of Parkinson\u2019s disease based on multi-modal features and stacking ensemble learning. J. Neurosci. Methods. 350, 109019 (2021).","journal-title":"J. Neurosci. Methods"},{"key":"44461_CR62","doi-asserted-by":"crossref","unstructured":"James, G. et al. An Introduction to Statistical Learning 112 (Springer, 2013).","DOI":"10.1007\/978-1-4614-7138-7"},{"issue":"2","key":"44461_CR63","first-page":"1","volume":"15","author":"M \u015eahin","year":"2013","unstructured":"\u015eahin, M., Kizilaslan, R. & Demirel, \u00d6. F. Forecasting aviation spare parts demand using croston based methods and artificial neural networks. J. Econ. Soc. Res. 15 (2), 1 (2013).","journal-title":"J. Econ. Soc. Res."},{"issue":"1","key":"44461_CR64","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10462-024-10989-8","volume":"58","author":"X Song","year":"2024","unstructured":"Song, X. et al. Deep learning-based time series forecasting. Artif. Intell. Rev. 58 (1), 23 (2024).","journal-title":"Artif. Intell. Rev."},{"issue":"7","key":"44461_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11223-9","volume":"58","author":"J Kim","year":"2025","unstructured":"Kim, J. et al. A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges. Artif. Intell. Rev. 58 (7), 1\u201395 (2025).","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"44461_CR66","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1016\/j.ijforecast.2020.02.005","volume":"36","author":"J Huber","year":"2020","unstructured":"Huber, J. & Stuckenschmidt, H. Daily retail demand forecasting using machine learning with emphasis on calendric special days. Int. J. Forecast. 36 (4), 1420\u20131438 (2020).","journal-title":"Int. J. Forecast."},{"key":"44461_CR67","doi-asserted-by":"publisher","first-page":"7183","DOI":"10.1029\/2000JD900719","volume":"106","author":"KE Taylor","year":"2001","unstructured":"Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. research: Atmos. 106, 7183\u20137192 (2001).","journal-title":"J. Geophys. research: Atmos."},{"issue":"1","key":"44461_CR68","doi-asserted-by":"publisher","first-page":"106","DOI":"10.29128\/geomatik.1374383","volume":"9","author":"T Demirg\u00fcl","year":"2024","unstructured":"Demirg\u00fcl, T., Demir, V. & Sevimli, M. F. Farkl\u0131 makine \u00f6\u011frenmesi yakla\u015f\u0131mlar\u0131 ile T\u00fcrkiye\u2019nin solar radyasyon tahmini. Geomatik 9 (1), 106\u2013122 (2024).","journal-title":"Geomatik"},{"issue":"Jan","key":"44461_CR69","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (Jan), 1\u201330 (2006).","journal-title":"J. Mach. Learn. Res."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44461-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44461-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44461-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T01:02:41Z","timestamp":1779238961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44461-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["44461"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-44461-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"28 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15661"}}