{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:57:22Z","timestamp":1777489042703,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"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":["Evol. Intel."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s12065-025-01078-y","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T12:01:00Z","timestamp":1754481660000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dendritic neuron model optimized with mountain gazelle optimizer for time-series forecasting"],"prefix":"10.1007","volume":"18","author":[{"given":"Mohammed A. A.","family":"Al-qaness","sequence":"first","affiliation":[]},{"given":"Ahmed A.","family":"Ewees","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Abd Elaziz","sequence":"additional","affiliation":[]},{"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[]},{"given":"Ahmed H.","family":"Samak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"1078_CR1","volume":"236","author":"W Yuhan","year":"2024","unstructured":"Yuhan W, Meng X, Zhang J, He Y, Romo JA, Dong Y, Dongming L (2024) Effective lstms with seasonal-trend decomposition and adaptive learning and niching-based backtracking search algorithm for time series forecasting. Expert Syst Appl 236:121202","journal-title":"Expert Syst Appl"},{"key":"1078_CR2","doi-asserted-by":"crossref","first-page":"16453","DOI":"10.1007\/s00500-020-04954-0","volume":"24","author":"P Hewage","year":"2020","unstructured":"Hewage P, Behera A, Trovati M, Pereira E, Ghahremani M, Palmieri F, Liu Y (2020) Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station. Soft Comput 24:16453\u201316482","journal-title":"Soft Comput"},{"key":"1078_CR3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108218","volume":"121","author":"D Cheng","year":"2022","unstructured":"Cheng D, Yang F, Xiang S, Liu J (2022) Financial time series forecasting with multi-modality graph neural network. Pattern Recogn 121:108218","journal-title":"Pattern Recogn"},{"key":"1078_CR4","volume":"28","author":"HB Kibria","year":"2022","unstructured":"Kibria HB, Jyoti O, Matin A (2022) Forecasting the spread of the third wave of covid-19 pandemic using time series analysis in bangladesh. Inform in Med Unlocked 28:100815","journal-title":"Inform in Med Unlocked"},{"issue":"2","key":"1078_CR5","doi-asserted-by":"crossref","first-page":"476","DOI":"10.3390\/math11020476","volume":"11","author":"MAA Al-qaness","year":"2023","unstructured":"Al-qaness MAA, Dahou A, Ewees AA, Abualigah L, Huai J, Elaziz MA, Helmi AM (2023) Resinformer: residual transformer-based artificial time-series forecasting model for pm2. 5 concentration in three major chinese cities. Math 11(2):476","journal-title":"Math"},{"issue":"1","key":"1078_CR6","volume":"2","author":"Y Ensafi","year":"2022","unstructured":"Ensafi Y, Amin SH, Zhang G, Shah B (2022) Time-series forecasting of seasonal items sales using machine learning-a comparative analysis. Int J of Inf Manag Data Insights 2(1):100058","journal-title":"Int J of Inf Manag Data Insights"},{"key":"1078_CR7","volume":"231","author":"C Gao","year":"2023","unstructured":"Gao C, Zhang N, Li Y, Lin Y, Wan H (2023) Adversarial self-attentive time-variant neural networks for multi-step time series forecasting. Expert Syst Appl 231:120722","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1078_CR8","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1007\/s10462-022-10199-0","volume":"56","author":"Z Hajirahimi","year":"2023","unstructured":"Hajirahimi Z, Khashei M (2023) Hybridization of hybrid structures for time series forecasting: a review. Artif Intell Rev 56(2):1201\u20131261","journal-title":"Artif Intell Rev"},{"issue":"16","key":"1078_CR9","first-page":"5","volume":"16","author":"L Bisaglia","year":"2014","unstructured":"Bisaglia L, Gerolimetto M (2014) Testing for (non) linearity in economic time series: a monte carlo comparison. Quaderni Di Statistica 16(16):5\u201332","journal-title":"Quaderni Di Statistica"},{"key":"1078_CR10","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jhydrol.2012.11.017","volume":"476","author":"M Valipour","year":"2013","unstructured":"Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the arma, arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of dez dam reservoir. J Hydrol 476:433\u2013441","journal-title":"J Hydrol"},{"issue":"1","key":"1078_CR11","first-page":"17","volume":"4","author":"A Altan","year":"2019","unstructured":"Altan A, Karasu S (2019) The effect of kernel values in support vector machine to forecasting performance of financial time series. The J Cognitive Syst 4(1):17\u201321","journal-title":"The J Cognitive Syst"},{"key":"1078_CR12","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.knosys.2018.10.009","volume":"163","author":"A Galicia","year":"2019","unstructured":"Galicia A, Talavera-Llames R, Troncoso A, Koprinska I, Mart\u00ednez-\u00c1lvarez F (2019) Multi-step forecasting for big data time series based on ensemble learning. Knowl-Based Syst 163:830\u2013841","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1078_CR13","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/joes.12429","volume":"37","author":"RP Masini","year":"2023","unstructured":"Masini RP, Medeiros MC, Mendes EF (2023) Machine learning advances for time series forecasting. J of Econ Surv 37(1):76\u2013111","journal-title":"J of Econ Surv"},{"key":"1078_CR14","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.renene.2021.12.056","volume":"185","author":"R Rahimilarki","year":"2022","unstructured":"Rahimilarki R, Gao Z, Jin N, Zhang A (2022) Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine. Renew Energy 185:916\u2013931","journal-title":"Renew Energy"},{"key":"1078_CR15","volume":"13","author":"ANMF Faisal","year":"2022","unstructured":"Faisal ANMF, Rahman A, Habib MTM, Siddique AH, Hasan M, Khan MM (2022) Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of bangladesh. Results in Eng 13:100365","journal-title":"Results in Eng"},{"key":"1078_CR16","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1795","volume":"10","author":"R Damasevicius","year":"2024","unstructured":"Damasevicius R, Jovanovic L, Petrovic A, Zivkovic M, Bacanin N, Jovanovic D, Antonijevic M (2024) Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation. PeerJ Comput Sci 10:e1795","journal-title":"PeerJ Comput Sci"},{"issue":"2","key":"1078_CR17","first-page":"336","volume":"52","author":"M Xingyu","year":"2023","unstructured":"Xingyu M (2023) Forecasting secondhand tanker price through wavelet neural networks based on adaptive genetic algorithm. Inf Technol and Control 52(2):336\u2013357","journal-title":"Inf Technol and Control"},{"issue":"3","key":"1078_CR18","first-page":"653","volume":"52","author":"S Yin","year":"2023","unstructured":"Yin S, Gao Y, Nie S, Li J (2023) Sstp: stock sector trend prediction with temporal-spatial network. Inf Technol and Control 52(3):653\u2013664","journal-title":"Inf Technol and Control"},{"key":"1078_CR19","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1016\/j.procs.2023.01.116","volume":"218","author":"B Sirisha","year":"2023","unstructured":"Sirisha B, Naveena S, Palanki G, Snehaa P (2023) Multivariate time series sensor feature forecasting using deep bidirectional lstm. Procedia Comput Sci 218:1374\u20131383","journal-title":"Procedia Comput Sci"},{"key":"1078_CR20","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.knosys.2016.05.031","volume":"105","author":"T Zhou","year":"2016","unstructured":"Zhou T, Gao S, Wang J, Chu C, Todo Y, Tang Z (2016) Financial time series prediction using a dendritic neuron model. Knowl-Based Syst 105:214\u2013224","journal-title":"Knowl-Based Syst"},{"issue":"9","key":"1078_CR21","first-page":"1128","volume":"26","author":"RK Behera","year":"2020","unstructured":"Behera RK, Das S, Rath SK, Misra S, Damasevicius R (2020) Comparative study of real time machine learning models for stock prediction through streaming data. J Univ Comput Sci 26(9):1128\u20131147","journal-title":"J Univ Comput Sci"},{"key":"1078_CR22","volume":"28","author":"SG Quek","year":"2022","unstructured":"Quek SG, Selvachandran G, Tan JH, Thiang HYA, Tuan NT et al (2022) A new hybrid model of fuzzy time series and genetic algorithm based machine learning algorithm: a case study of forecasting prices of nine types of major cryptocurrencies. Big Data Res 28:100315","journal-title":"Big Data Res"},{"key":"1078_CR23","volume":"116","author":"X Xinghan","year":"2022","unstructured":"Xinghan X, Ren W (2022) A hybrid model of stacked autoencoder and modified particle swarm optimization for multivariate chaotic time series forecasting. Appl Soft Comput 116:108321","journal-title":"Appl Soft Comput"},{"key":"1078_CR24","volume":"102","author":"ZIE Cicek","year":"2021","unstructured":"Cicek ZIE, Ozturk ZK (2021) Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting. Appl Soft Comput 102:107091","journal-title":"Appl Soft Comput"},{"key":"1078_CR25","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.neucom.2021.08.153","volume":"489","author":"J Ji","year":"2022","unstructured":"Ji J, Tang C, Zhao J, Tang Z, Todo Y (2022) A survey on dendritic neuron model: mechanisms, algorithms and practical applications. Neurocomputing 489:390\u2013406","journal-title":"Neurocomputing"},{"key":"1078_CR26","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.ins.2022.06.012","volume":"607","author":"E Egrioglu","year":"2022","unstructured":"Egrioglu E, Ba\u015f E, Chen M-Y (2022) Recurrent dendritic neuron model artificial neural network for time series forecasting. Inf Sci 607:572\u2013584","journal-title":"Inf Sci"},{"key":"1078_CR27","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117637","volume":"205","author":"Y Tang","year":"2022","unstructured":"Tang Y, Song Z, Zhu Y, Hou M, Tang C, Ji J (2022) Adopting a dendritic neural model for predicting stock price index movement. Expert Syst Appl 205:117637","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1078_CR28","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s00521-022-07775-4","volume":"35","author":"O Altay","year":"2023","unstructured":"Altay O, Altay EV (2023) A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer. Neural Comput Appl 35(1):529\u2013556","journal-title":"Neural Comput Appl"},{"key":"1078_CR29","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122349","volume":"239","author":"Z Yang","year":"2024","unstructured":"Yang Z (2024) Competing leaders grey wolf optimizer and its application for training multi-layer perceptron classifier. Expert Syst Appl 239:122349","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1078_CR30","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1007\/s12530-023-09518-9","volume":"15","author":"D Chauhan","year":"2024","unstructured":"Chauhan D, Yadav A, Neri F (2024) A multi-agent optimization algorithm and its application to training multilayer perceptron models. Evol Syst 15(3):849\u2013879","journal-title":"Evol Syst"},{"issue":"4","key":"1078_CR31","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1002\/for.2833","volume":"41","author":"A Yilmaz","year":"2022","unstructured":"Yilmaz A, Yolcu U (2022) Dendritic neuron model neural network trained by modified particle swarm optimization for time-series forecasting. J Forecast 41(4):793\u2013809","journal-title":"J Forecast"},{"key":"1078_CR32","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ins.2023.02.008","volume":"629","author":"HH Gul","year":"2023","unstructured":"Gul HH, Egrioglu E, Bas E (2023) Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm. Inf Sci 629:398\u2013412","journal-title":"Inf Sci"},{"key":"1078_CR33","volume":"2025","author":"X Weixiang","year":"2021","unstructured":"Weixiang X, Li C, Dou Y, Zhang M, Dong Z, Jia D, Ban X (2021) Optimizing the weights and thresholds in dendritic neuron model using the whale optimization algorithm. J Phys: Conf Ser 2025:012037 (IOP Publishing)","journal-title":"J Phys: Conf Ser"},{"issue":"24","key":"1078_CR34","doi-asserted-by":"crossref","first-page":"9261","DOI":"10.3390\/en15249261","volume":"15","author":"MAA Al-qaness","year":"2022","unstructured":"Al-qaness MAA, Ewees AA, Elaziz MA, Samak AH (2022) Wind power forecasting using optimized dendritic neural model based on seagull optimization algorithm and aquila optimizer. Energies 15(24):9261","journal-title":"Energies"},{"key":"1078_CR35","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.asoc.2018.03.033","volume":"68","author":"F Arce","year":"2018","unstructured":"Arce F, Zamora E, Sossa H, Barr\u00f3n R (2018) Differential evolution training algorithm for dendrite morphological neural networks. Appl Soft Comput 68:303\u2013313","journal-title":"Appl Soft Comput"},{"key":"1078_CR36","doi-asserted-by":"crossref","first-page":"130921","DOI":"10.1109\/ACCESS.2022.3229314","volume":"10","author":"SC Nayak","year":"2022","unstructured":"Nayak SC, Dehuri S, Cho S-B (2022) Intelligent financial forecasting with an improved chemical reaction optimization algorithm based dendritic neuron model. IEEE Access 10:130921\u2013130943","journal-title":"IEEE Access"},{"key":"1078_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.advengsoft.2022.103282","volume":"174","author":"B Abdollahzadeh","year":"2022","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Khodadadi N, Mirjalili S (2022) Mountain gazelle optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Adv Eng Softw 174:103282","journal-title":"Adv Eng Softw"},{"key":"1078_CR38","volume":"111","author":"M Dong","year":"2021","unstructured":"Dong M, Tang C, Ji J, Lin Q, Wong K-C (2021) Transmission trend of the covid-19 pandemic predicted by dendritic neural regression. Appl Soft Comput 111:107683","journal-title":"Appl Soft Comput"},{"key":"1078_CR39","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks. volume\u00a04, pp 1942\u20131948. ieee","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1078_CR40","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133","journal-title":"Knowl-Based Syst"},{"key":"1078_CR41","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228\u2013249","journal-title":"Knowl-Based Syst"},{"key":"1078_CR42","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"1078_CR43","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849\u2013872","journal-title":"Futur Gener Comput Syst"},{"key":"1078_CR44","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC), pp 1658\u20131665. IEEE","DOI":"10.1109\/CEC.2014.6900380"},{"key":"1078_CR45","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128427","volume":"607","author":"C Yuan","year":"2024","unstructured":"Yuan C, Zhao D, Heidari AA, Liu L, Chen Y, Chen H (2024) Polar lights optimizer: algorithm and applications in image segmentation and feature selection. Neurocomputing 607:128427","journal-title":"Neurocomputing"},{"key":"1078_CR46","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107532","volume":"128","author":"R Sowmya","year":"2024","unstructured":"Sowmya R, Premkumar M, Jangir P (2024) Newton-raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems. Eng Appl Artif Intell 128:107532","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1078_CR47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-024-00985-w","volume":"18","author":"X Wang","year":"2025","unstructured":"Wang X (2025) Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems. Evol Intel 18(1):1\u201320","journal-title":"Evol Intel"},{"issue":"200","key":"1078_CR48","doi-asserted-by":"crossref","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(200):675\u2013701","journal-title":"J Am Stat Assoc"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01078-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-025-01078-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01078-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:17:48Z","timestamp":1761394668000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-025-01078-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["1078"],"URL":"https:\/\/doi.org\/10.1007\/s12065-025-01078-y","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"assertion":[{"value":"23 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2025","order":4,"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"}},{"value":"Not applicable (This article does not contain studies involving human or animal participants).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"92"}}