{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:55:21Z","timestamp":1781103321739,"version":"3.54.1"},"reference-count":30,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,7,1]]},"abstract":"<p>Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward as well as recurrent neural networks through training with PSO. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are used to train feedforward ANN (FANN) and Elman ANN (EANN) models. A novel nonlinear hybrid architecture is proposed to incorporate the training strengths of all these three PSO algorithms. Experiments are conducted on four real-world time series with the three forecasting models, viz. Box-Jenkins, FANN and EANN. Obtained results clearly demonstrate the superior forecasting performances of all three PSO algorithms over their BP counterpart for both FANN as well as EANN models. Both PSO and BP based neural networks also achieved notably better accuracies than the statistical Box-Jenkins methods. The forecasting performances of the neural network models are further improved through the proposed hybrid PSO framework.<\/p>","DOI":"10.4018\/jaec.2013070107","type":"journal-article","created":{"date-parts":[[2013,12,5]],"date-time":"2013-12-05T11:54:29Z","timestamp":1386244469000},"page":"75-90","source":"Crossref","is-referenced-by-count":12,"title":["Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting"],"prefix":"10.4018","volume":"4","author":[{"given":"Ratnadip","family":"Adhikari","sequence":"first","affiliation":[{"name":"Jawaharlal Nehru University, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. K.","family":"Agrawal","sequence":"additional","affiliation":[{"name":"School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jaec.2013070107-0","unstructured":"Adhikari, R., & Agrawal, R. K. (2011). Effectiveness of PSO based neural network for seasonal time series forecasting. In Proceedings of the Indian International Conference on Artificial Intelligence (IICAI), Tumkur, India (pp. 232\u2013244)."},{"key":"jaec.2013070107-1","doi-asserted-by":"crossref","unstructured":"Adhikari, R., & Agrawal, R. K. (2012). A novel weighted ensemble technique for time series forecasting. In Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kuala Lumpur, Malaysia (pp. 38\u201349).","DOI":"10.1007\/978-3-642-30217-6_4"},{"key":"jaec.2013070107-2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.2.141"},{"key":"jaec.2013070107-3","doi-asserted-by":"crossref","unstructured":"Birge, B. (2003). PSOt-A particle swarm optimization toolbox for use with Matlab. In Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, IN (pp. 182-186).","DOI":"10.1109\/SIS.2003.1202265"},{"key":"jaec.2013070107-4","author":"G. E.Box","year":"1970","journal-title":"Time series analysis, forecasting and control"},{"key":"jaec.2013070107-5","unstructured":"Chen, A. P., Huang, C. H., & Hsu, Y. C. (2011). Particle swarm optimization with inertia weight and constriction factor. In Proceedings of the International Conference on Swarm Intelligence (ICSI), Cergy, France (pp. 1\u201311)."},{"key":"jaec.2013070107-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.02.006"},{"key":"jaec.2013070107-7","doi-asserted-by":"publisher","DOI":"10.1109\/4235.985692"},{"key":"jaec.2013070107-8","doi-asserted-by":"publisher","DOI":"10.1007\/BF02551274"},{"key":"jaec.2013070107-9","author":"H.Demuth","year":"2010","journal-title":"Neural network toolbox user's guide"},{"key":"jaec.2013070107-10","author":"J. E.Dennis","year":"1983","journal-title":"Numerical methods for unconstrained optimization and nonlinear equations"},{"key":"jaec.2013070107-11","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9876.00109"},{"key":"jaec.2013070107-12","doi-asserted-by":"publisher","DOI":"10.1109\/72.329697"},{"key":"jaec.2013070107-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.02.042"},{"key":"jaec.2013070107-14","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"jaec.2013070107-15","unstructured":"Hyndman, R. J. (2011). Time series data library (TSDL). Retrieved from http:\/\/robjhyndman.com\/TSDL\/"},{"key":"jaec.2013070107-16","doi-asserted-by":"crossref","unstructured":"Jha, G. K., Thulasiraman, P., & Thulasiram, R. K. (2009). PSO based neural network for time series forecasting. In Proceedings of the IEEE International Joint Conference on Neural Networks, Atlanta, GA (pp. 1422\u20131427).","DOI":"10.1109\/IJCNN.2009.5178707"},{"key":"jaec.2013070107-17","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-59140-670-9"},{"key":"jaec.2013070107-18","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (ICNN), Piscataway, NJ (pp. 1942\u20131948).","DOI":"10.1109\/ICNN.1995.488968"},{"key":"jaec.2013070107-19","author":"J.Kennedy","year":"2001","journal-title":"Swarm intelligence"},{"key":"jaec.2013070107-20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.044"},{"issue":"2","key":"jaec.2013070107-21","first-page":"41","article-title":"Seasonal time series forecasting: A comparative study of ARIMA and ANN models.","volume":"5","author":"J.Kihoro","year":"2004","journal-title":"African Journal of Science and Technology"},{"key":"jaec.2013070107-22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2009.09.020"},{"key":"jaec.2013070107-23","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2002.804317"},{"key":"jaec.2013070107-24","first-page":"119","article-title":"The application of an ensemble of boosted Elman networks to time series prediction: A benchmark study.","volume":"3","author":"C. P.Lim","year":"2005","journal-title":"Journal of Computational Intelligence"},{"key":"jaec.2013070107-25","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80056-5"},{"key":"jaec.2013070107-26","doi-asserted-by":"crossref","unstructured":"Reidmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The rprop algorithm. In Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, CA (pp. 586\u2013591).","DOI":"10.1109\/ICNN.1993.298623"},{"key":"jaec.2013070107-27","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-0190(02)00447-7"},{"key":"jaec.2013070107-28","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2070(97)00044-7"},{"key":"jaec.2013070107-29","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"}],"container-title":["International Journal of Applied Evolutionary Computation"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=95960","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T23:19:40Z","timestamp":1654125580000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jaec.2013070107"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2013,7,1]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2013,7]]}},"URL":"https:\/\/doi.org\/10.4018\/jaec.2013070107","relation":{},"ISSN":["1942-3594","1942-3608"],"issn-type":[{"value":"1942-3594","type":"print"},{"value":"1942-3608","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,7,1]]}}}