{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:41:01Z","timestamp":1765438861148,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100009043","name":"University of Patras","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009043","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the contemporary context, both production and consumption of energy, being concepts intertwined through a condition of synchronicity, are pivotal for the orderly functioning of society, with their management being a building block in maintaining regularity. Hence, the pursuit to develop reliable computational tools for modeling such serial and time-dependent phenomena becomes similarly crucial. This paper investigates the use of ensemble learners for medium-term forecasting of the Greek energy system load using additional information from injected energy production from various sources. Through an extensive experimental process, over 435 regression schemes and 64 different modifications of the feature inputs were tested over five different prediction time frames, creating comparative rankings regarding two case studies: one related to methods and the other to feature setups. Evaluations according to six widely used metrics indicate an aggregate but clear dominance of a specific efficient and low-cost ensemble layout. In particular, an ensemble method that incorporates the <jats:italic>orthogonal matching pursuit<\/jats:italic> together with the <jats:italic>Huber regressor<\/jats:italic> according to an averaged combinatorial scheme is proposed. Moreover, it is shown that the use of multivariate setups improves the derived predictions.<\/jats:p>","DOI":"10.1007\/s00521-023-08777-6","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T02:01:31Z","timestamp":1688954491000},"page":"21479-21497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A multivariate ensemble learning method for medium-term energy forecasting"],"prefix":"10.1007","volume":"35","author":[{"given":"Charalampos M.","family":"Liapis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aikaterini","family":"Karanikola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"8777_CR1","doi-asserted-by":"publisher","first-page":"105616","DOI":"10.1016\/j.asoc.2019.105616","volume":"83","author":"S Maldonado","year":"2019","unstructured":"Maldonado S, Gonz\u00e1lez A, Crone S (2019) Automatic time series analysis for electric load forecasting via support vector regression. Appl Soft Comput 83:105616. https:\/\/doi.org\/10.1016\/j.asoc.2019.105616","journal-title":"Appl Soft Comput"},{"key":"8777_CR2","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TPWRS.2005.860944","volume":"21","author":"S Fan","year":"2006","unstructured":"Fan S, Chen L (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21:392\u2013401","journal-title":"IEEE Trans Power Syst"},{"key":"8777_CR3","doi-asserted-by":"publisher","unstructured":"Moon J, Kim Y, Son M, Hwang E (2018) Hybrid short-term load forecasting scheme using random forest and multilayer perceptron. Energies 11(12). https:\/\/doi.org\/10.3390\/en11123283","DOI":"10.3390\/en11123283"},{"issue":"1","key":"8777_CR4","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1080\/01430750.2019.1682041","volume":"43","author":"F Li","year":"2022","unstructured":"Li F, Jin G (2022) Research on power energy load forecasting method based on knn. Int J Ambient Energy 43(1):946\u2013951. https:\/\/doi.org\/10.1080\/01430750.2019.1682041","journal-title":"Int J Ambient Energy"},{"key":"8777_CR5","doi-asserted-by":"publisher","unstructured":"Nepal B, Yamaha M, Yokoe A, Yamaji T (2019) Electricity load forecasting using clustering and arima model for energy management in buildings. Japan Architectural Review, 3(1):62\u201376. https:\/\/doi.org\/10.1002\/2475-8876.12135","DOI":"10.1002\/2475-8876.12135"},{"key":"8777_CR6","doi-asserted-by":"crossref","unstructured":"Abbasi RA, Javaid N, Ghuman MNJ, Khan ZA, Ur Rehman S, Amanullah, (2019) Short term load forecasting using xgboost. In: Barolli L, Takizawa M, Xhafa F, Enokido T (eds) Web, artificial intelligence and network applications. Springer, Cham, pp 1120\u20131131","DOI":"10.1007\/978-3-030-15035-8_108"},{"issue":"1","key":"8777_CR7","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TPWRS.2004.840380","volume":"20","author":"AJR Reis","year":"2005","unstructured":"Reis AJR, da Silva APA (2005) Feature extraction via multiresolution analysis for short-term load forecasting. IEEE Trans Power Syst 20(1):189\u2013198. https:\/\/doi.org\/10.1109\/TPWRS.2004.840380","journal-title":"IEEE Trans Power Syst"},{"key":"8777_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115440","volume":"276","author":"B Dietrich","year":"2020","unstructured":"Dietrich B, Walther J, Weigold M, Abele E (2020) Machine learning based very short term load forecasting of machine tools. Appl Energy 276:115440. https:\/\/doi.org\/10.1016\/j.apenergy.2020.115440","journal-title":"Appl Energy"},{"key":"8777_CR9","doi-asserted-by":"crossref","unstructured":"Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using deep neural networks. In: IECON 2016\u201442nd Annual Conference of the IEEE Industrial Electronics Society, pp 7046\u20137051","DOI":"10.1109\/IECON.2016.7793413"},{"key":"8777_CR10","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2019","unstructured":"Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2019) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Trans Smart Grid 10:841\u2013851","journal-title":"IEEE Trans Smart Grid"},{"key":"8777_CR11","doi-asserted-by":"publisher","unstructured":"Bouktif S, Fiaz A, Ouni A, Serhani MA (2018) Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 11(7). https:\/\/doi.org\/10.3390\/en11071636","DOI":"10.3390\/en11071636"},{"key":"8777_CR12","doi-asserted-by":"publisher","first-page":"143759","DOI":"10.1109\/ACCESS.2020.3009537","volume":"8","author":"M Sajjad","year":"2020","unstructured":"Sajjad M, Khan ZA, Ullah A, Hussain T, Ullah W, Lee MY, Baik SW (2020) A novel cnn-gru-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759\u2013143768","journal-title":"IEEE Access"},{"key":"8777_CR13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/en11010213","volume":"11","author":"P-H Kuo","year":"2018","unstructured":"Kuo P-H, Huang C-J (2018) A high precision artificial neural networks model for short-term energy load forecasting. Energies 11:213","journal-title":"Energies"},{"key":"8777_CR14","doi-asserted-by":"crossref","unstructured":"Amarasinghe K, Marino DL, Manic M (2017) Deep neural networks for energy load forecasting. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), pp 1483\u20131488","DOI":"10.1109\/ISIE.2017.8001465"},{"key":"8777_CR15","doi-asserted-by":"crossref","unstructured":"He W (2017) Load forecasting via deep neural networks. In: International Conference on Information Technology and Quantitative Management","DOI":"10.1016\/j.procs.2017.11.374"},{"key":"8777_CR16","doi-asserted-by":"publisher","unstructured":"Kim J, Moon J, Hwang E, Kang P (2019) Recurrent inception convolution neural network for multi short-term load forecasting. Energy and Buildings 194:328\u2013341. https:\/\/doi.org\/10.1016\/j.enbuild.2019.04.034","DOI":"10.1016\/j.enbuild.2019.04.034"},{"key":"8777_CR17","doi-asserted-by":"publisher","first-page":"106025","DOI":"10.1016\/j.epsr.2019.106025","volume":"178","author":"G Sideratos","year":"2020","unstructured":"Sideratos G, Ikonomopoulos A, Hatziargyriou ND (2020) A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks. Electric Power Syst Res 178:106025","journal-title":"Electric Power Syst Res"},{"key":"8777_CR18","doi-asserted-by":"publisher","first-page":"36411","DOI":"10.1109\/ACCESS.2020.2975738","volume":"8","author":"L Sehovac","year":"2020","unstructured":"Sehovac L, Grolinger K (2020) Deep learning for load forecasting: Sequence to sequence recurrent neural networks with attention. IEEE Access 8:36411\u201336426","journal-title":"IEEE Access"},{"key":"8777_CR19","doi-asserted-by":"publisher","first-page":"109921","DOI":"10.1016\/j.enbuild.2020.109921","volume":"216","author":"J Moon","year":"2020","unstructured":"Moon J, Jung S-W, Rew J, Rho S, Hwang E (2020) Combination of short-term load forecasting models based on a stacking ensemble approach. Energy Build 216:109921","journal-title":"Energy Build"},{"key":"8777_CR20","doi-asserted-by":"publisher","unstructured":"Niu D, Yu M, Sun L, Gao T, Wang K (2022) Short-term multi-energy load forecasting for integrated energy systems based on cnn-bigru optimized by attention mechanism. Appl Energy 313:118801. https:\/\/doi.org\/10.1016\/j.apenergy.2022.118801","DOI":"10.1016\/j.apenergy.2022.118801"},{"key":"8777_CR21","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.ijforecast.2015.11.011","volume":"32","author":"T Hong","year":"2016","unstructured":"Hong T, Fan S (2016) Probabilistic electric load forecasting: a tutorial review. Int J Forecast 32:914\u2013938","journal-title":"Int J Forecast"},{"key":"8777_CR22","doi-asserted-by":"publisher","unstructured":"Fallah SN, Deo RC, Shojafar M, Conti M, Shamshirband S (2018) Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and research directions. Energies 11(3). https:\/\/doi.org\/10.3390\/en11030596","DOI":"10.3390\/en11030596"},{"key":"8777_CR23","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2478\/jlst-2020-0004","volume":"11","author":"MA Hammad","year":"2020","unstructured":"Hammad MA, Jereb B, Rosi B, Dragan D (2020) Methods and models for electric load forecasting: a comprehensive review. Logist Sustain Transp 11:51\u201376","journal-title":"Logist Sustain Transp"},{"key":"8777_CR24","doi-asserted-by":"crossref","unstructured":"Almalaq A, Edwards G (2017) A review of deep learning methods applied on load forecasting. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 511\u2013516","DOI":"10.1109\/ICMLA.2017.0-110"},{"key":"8777_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s43067-019-0008-x","volume":"7","author":"IK Nti","year":"2020","unstructured":"Nti IK, Teimeh M, Nyarko-Boateng O, Adekoya AF (2020) Electricity load forecasting: a systematic review. J Electr Syst Inf Technol 7:1\u201319","journal-title":"J Electr Syst Inf Technol"},{"key":"8777_CR26","unstructured":"Rousseeuw PJ, Hampel FR, Ronchetti EM, Stahel WA (2011) Robust statistics: the approach based on influence functions. Wiley Series in Probability and Statistics. Wiley, Nashville. https:\/\/books.google.gr\/books?id=XK3uhrVefXQC"},{"key":"8777_CR27","doi-asserted-by":"publisher","first-page":"105660","DOI":"10.1016\/j.jat.2021.105660","volume":"273","author":"Y Feng","year":"2022","unstructured":"Feng Y, Wu Q (2022) A statistical learning assessment of huber regression. J Approx Theory 273:105660. https:\/\/doi.org\/10.1016\/j.jat.2021.105660","journal-title":"J Approx Theory"},{"key":"8777_CR28","unstructured":"Rubinstein R, Zibulevsky M, Elad M (2008) Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit. CS Technion 40"},{"key":"8777_CR29","doi-asserted-by":"publisher","unstructured":"Skianis K, Tziortziotis N, Vazirgiannis M (2018) Orthogonal matching pursuit for text classification. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1807.04715. https:\/\/arxiv.org\/abs\/1807.04715","DOI":"10.48550\/ARXIV.1807.04715"},{"key":"8777_CR30","doi-asserted-by":"publisher","unstructured":"Needell D, Vershynin R (2007) Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.0707.4203. arxiv:https:\/\/arxiv.org\/abs\/0707.4203","DOI":"10.48550\/ARXIV.0707.4203"},{"key":"8777_CR31","doi-asserted-by":"crossref","unstructured":"Perrinet LU (2015) Sparse models for computer vision. ArXiv abs\/1701.06859","DOI":"10.1002\/9783527680863.ch14"},{"key":"8777_CR32","doi-asserted-by":"publisher","unstructured":"Gao X, Wang X, Zhou J (2020) A robust orthogonal matching pursuit based on l1 norm. In: 2020 Chinese control and decision conference (CCDC), pp 3735\u20133740. https:\/\/doi.org\/10.1109\/CCDC49329.2020.9164411","DOI":"10.1109\/CCDC49329.2020.9164411"},{"issue":"1","key":"8777_CR33","first-page":"78","volume":"27","author":"KM Banner","year":"2017","unstructured":"Banner KM, Higgs MD (2017) Considerations for assessing model averaging of regression coefficients. Ecol Appl Publ Ecol Soc Am 27(1):78\u201393","journal-title":"Ecol Appl Publ Ecol Soc Am"},{"key":"8777_CR34","doi-asserted-by":"crossref","unstructured":"Liapis CM, Karanikola A, Kotsiantis SB (2022) Energy load forecasting: Investigating mid-term predictions with ensemble learners. In: AIAI","DOI":"10.1007\/978-3-031-08333-4_28"},{"key":"8777_CR35","unstructured":"Gov.gr: Government of Greece\u2014Public Sector: Energy System Load (2022). https:\/\/www.data.gov.gr\/datasets\/admie_realtimescad\/ asystemload\/"},{"key":"8777_CR36","unstructured":"Gov.gr: Government of Greece\u2014Public Sector: Energy Balance (2022). https:\/\/www.data.gov.gr\/datasets\/admie_dailyenergybalanceanalysis\/"},{"key":"8777_CR37","unstructured":"Drucker H (1997) Improving regressors using boosting techniques. In: Proceedings of the Fourteenth International Conference on Machine Learning. ICML \u201997, pp 107\u2013115. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA"},{"key":"8777_CR38","unstructured":"Wipf D, Nagarajan S (2007) A new view of automatic relevance determination. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. NIPS\u201907, pp 1625\u20131632. Curran Associates Inc., Red Hook, NY, USA"},{"key":"8777_CR39","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","volume":"4","author":"DJC Mackay","year":"1992","unstructured":"Mackay DJC (1992) Bayesian interpolation. Neural Comput 4:415\u2013447","journal-title":"Neural Comput"},{"key":"8777_CR40","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: Unbiased boosting with categorical features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS\u201918, pp 6639\u20136649. Curran Associates Inc., Red Hook, NY, USA"},{"key":"8777_CR41","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth and Brooks, Monterey, CA"},{"issue":"2","key":"8777_CR42","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Series B (Stat Methodol) 67(2):301\u2013320","journal-title":"J R Stat Soc Series B (Stat Methodol)"},{"issue":"1","key":"8777_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach Learn"},{"key":"8777_CR44","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916, pp 785\u2013794. ACM, New York, NY, USA. https:\/\/doi.org\/10.1145\/2939672.2939785. http:\/\/doi.acm.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"8777_CR45","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189\u20131232. https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann Stat"},{"issue":"3","key":"8777_CR46","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1214\/aos\/1176325633","volume":"22","author":"L Devroye","year":"1994","unstructured":"Devroye L, Gyorfi L, Krzyzak A, Lugosi G (1994) On the strong universal consistency of nearest neighbor regression function estimates. Ann Stat 22(3):1371\u20131385. https:\/\/doi.org\/10.1214\/aos\/1176325633","journal-title":"Ann Stat"},{"key":"8777_CR47","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-642-41136-6_11","volume-title":"Empirical Inference","author":"V Vovk","year":"2013","unstructured":"Vovk V (2013) Kernel ridge regression. In: Sch\u00f6lkopf B, Luo Z, Vovk V (eds) Empirical Inference. Springer, Heidelberg, pp 105\u2013116"},{"issue":"2","key":"8777_CR48","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407\u2013451","journal-title":"Ann Stat"},{"key":"8777_CR49","doi-asserted-by":"publisher","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Series B (Methodol) 58(1):267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"8777_CR50","doi-asserted-by":"publisher","first-page":"105758","DOI":"10.1016\/j.agwat.2019.105758","volume":"225","author":"J Fan","year":"2019","unstructured":"Fan J, Ma X, Wu L, Zhang F, Yu X, Zeng W (2019) Light gradient boosting machine: an efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric Water Manag 225:105758. https:\/\/doi.org\/10.1016\/j.agwat.2019.105758","journal-title":"Agric Water Manag"},{"key":"8777_CR51","volume-title":"Linear regression analysis. Wiley series in probability and statistics","author":"GAF Seber","year":"2012","unstructured":"Seber GAF, Lee AJ (2012) Linear regression analysis. Wiley series in probability and statistics. Wiley, New York"},{"issue":"5","key":"8777_CR52","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 (1991) Multilayer perceptrons for classification and regression. Neurocomputing 2(5):183\u2013197. https:\/\/doi.org\/10.1016\/0925-2312(91)90023-5","journal-title":"Neurocomputing"},{"key":"8777_CR53","first-page":"551","volume":"7","author":"K Crammer","year":"2006","unstructured":"Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551\u2013585","journal-title":"J Mach Learn Res"},{"issue":"1","key":"8777_CR54","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"8777_CR55","doi-asserted-by":"publisher","unstructured":"Choi S, Kim T, Yu W (2009) Performance evaluation of ransac family, vol. 24. https:\/\/doi.org\/10.5244\/C.23.81","DOI":"10.5244\/C.23.81"},{"key":"8777_CR56","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/00031305.1975.10479105","volume":"29","author":"D Marquardt","year":"1975","unstructured":"Marquardt D, Snee R (1975) Ridge regression in practice. Am Stat AMER STATIST 29:3\u201320. https:\/\/doi.org\/10.1080\/00031305.1975.10479105","journal-title":"Am Stat AMER STATIST"},{"issue":"3","key":"8777_CR57","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222. https:\/\/doi.org\/10.1023\/B:STCO.0000035301.49549.88","journal-title":"Stat Comput"},{"key":"8777_CR58","unstructured":"Dang X, Peng H, Wang X, Zhang H (2009) Theil-Sen estimators in a multiple linear regression model"},{"key":"8777_CR59","unstructured":"Ali M (2020) PyCaret: an open source, low-code machine learning library in Python. PyCaret version 1.0.0. https:\/\/www.pycaret.org"},{"key":"8777_CR60","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675\u2013701","journal-title":"J Am Stat Assoc"},{"key":"8777_CR61","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56:52\u201364","journal-title":"J Am Stat Assoc"},{"key":"8777_CR62","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez I, Canosa A, Mucientes M, Bugar\u00edn-Diz A (2015) Stac: A web platform for the comparison of algorithms using statistical tests. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1\u20138","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08777-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08777-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08777-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T14:06:18Z","timestamp":1694873178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08777-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":62,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8777"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08777-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,7,10]]},"assertion":[{"value":"26 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}