{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:16:34Z","timestamp":1759364194810,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032027245","type":"print"},{"value":"9783032027252","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02725-2_47","type":"book-chapter","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:37:49Z","timestamp":1759279069000},"page":"601-615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting Non-stationary Time Series: A Comparison of\u00a0Deep and\u00a0Shallow Neural Network Architectures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1296-6972","authenticated-orcid":false,"given":"Takudzwa","family":"Masunungure","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0242-3539","authenticated-orcid":false,"given":"Andries","family":"Engelbrecht","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"7","key":"47_CR1","doi-asserted-by":"publisher","first-page":"2667","DOI":"10.1007\/s00521-020-05163-4","volume":"33","author":"SA Abdulkarim","year":"2021","unstructured":"Abdulkarim, S.A., Engelbrecht, A.P.: Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments. Neural Comput. Appl. 33(7), 2667\u20132683 (2021)","journal-title":"Neural Comput. Appl."},{"key":"47_CR2","doi-asserted-by":"crossref","unstructured":"Adhikari, R., Agrawal, R.K., Kant, L.: Particle Swarm Optimization based neural networks vs. traditional statistical models for seasonal time series forecasting. In Proceedings of the Third IEEE International Advance Computing Conference, pp. 719\u2013725 (2013)","DOI":"10.1109\/IAdCC.2013.6514315"},{"issue":"6","key":"47_CR3","first-page":"685","volume":"38","author":"H Allende","year":"2002","unstructured":"Allende, H., Moraga, C., Salas, R.: Artificial neural networks in time series forecasting: a comparative analysis. Kybernetika 38(6), 685\u2013707 (2002)","journal-title":"Kybernetika"},{"key":"47_CR4","doi-asserted-by":"crossref","unstructured":"Al-kazemi, B., Mohan, C.K.: Training feedforward neural networks using multi-phase particle swarm optimization. In: Proceedings of the IEEE 9th International Conference on Neural Information Processing, pp. 2615\u20132619 (2002)","DOI":"10.1109\/ICONIP.2002.1201969"},{"key":"47_CR5","unstructured":"Becker, S., Le Cun, Y.: Improving the convergence of back-propagation learning with second order methods. In: Proceedings of the Connectionist Models Summer School, pp. 29\u201337 (1988)"},{"issue":"4","key":"47_CR6","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1109\/TEVC.2005.857074","volume":"10","author":"T Blackwell","year":"2006","unstructured":"Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans. Evol. Comput. 10(4), 459\u2013472 (2006)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"47_CR7","unstructured":"Box, G.: EP and Jenkins. G. M, Time series analysis, Holden Day, San Francisco (1970)"},{"issue":"3","key":"47_CR8","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.: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86(3), 116\u2013123 (2012)","journal-title":"Neurocomputing"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"47_CR10","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s11063-020-10290-z","volume":"52","author":"C Dennis","year":"2020","unstructured":"Dennis, C., Engelbrecht, A.P., Ombuki-Berman, B.M.: An analysis of activation function saturation in particle swarm optimization trained neural networks. Neural Process. Lett. 52, 1123\u20131153 (2020)","journal-title":"Neural Process. Lett."},{"issue":"1","key":"47_CR11","doi-asserted-by":"publisher","first-page":"1250010","DOI":"10.1142\/S0218213011000462","volume":"21","author":"JP Donate","year":"2012","unstructured":"Donate, J.P., Sanchez, G.G., De Miguel, A.S.: Time series forecasting. A comparative study between an evolving artificial neural networks system and statistical methods. Int. J. Artif. Intell. Tools 21(1), 1250010 (2012)","journal-title":"Int. J. Artif. Intell. Tools"},{"issue":"4","key":"47_CR12","first-page":"447","volume":"6","author":"G Dorffner","year":"1996","unstructured":"Dorffner, G.: Neural networks for time series processing. Neural Netw. World 6(4), 447\u2013468 (1996)","journal-title":"Neural Netw. World"},{"issue":"2","key":"47_CR13","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"issue":"3","key":"47_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/BF00342633","volume":"20","author":"K Fukushima","year":"1975","unstructured":"Fukushima, K.: Cognitron: a self-organizing multilayered neural network. Biol. Cybern. 20(3), 121\u2013136 (1975)","journal-title":"Biol. Cybern."},{"key":"47_CR15","doi-asserted-by":"crossref","unstructured":"Garcia-Pedrero, A., Gomez-Gil, P.: Time series forecasting using recurrent neural networks and wavelet reconstructed signals. In: Proccedings of the 20th International Conference on Electronics Communications and Computers, pp. 169\u2013173 (2010)","DOI":"10.1109\/CONIELECOMP.2010.5440775"},{"key":"47_CR16","unstructured":"G\u00e9ron, A.: Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (Third edition). O\u2019Reilly Media, Inc (2022)"},{"key":"47_CR17","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Convolutional networks. Deep Learning, pp. 330\u2013372. MIT Press (2016)"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Gudise, V.G., Venayagamoorthy, G.K.: Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 110\u2013117 (2003)","DOI":"10.1109\/SIS.2003.1202255"},{"key":"47_CR19","doi-asserted-by":"crossref","unstructured":"Guo, W., Qiao, Y., Hou, H.: BP neural network optimized with PSO algorithm and its application in forecasting. In: Proceedings of the IEEE International Conference on Information Acquisition, pp. 617\u2013621 (2006)","DOI":"10.1109\/ICIA.2006.305796"},{"issue":"1","key":"47_CR20","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s11721-017-0150-9","volume":"12","author":"KR Harrison","year":"2018","unstructured":"Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intell. 12(1), 187\u2013226 (2018)","journal-title":"Swarm Intell."},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"issue":"8","key":"47_CR22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Jha, G.K., Thulasiraman, P., Thulasiram, R.K.: PSO based neural network for time series forecasting. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1422\u20131427 (2009)","DOI":"10.1109\/IJCNN.2009.5178707"},{"key":"47_CR24","unstructured":"Jordan, M.: Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pp. 513\u2013546 (1986)"},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942\u20131948 (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1671\u20131676 (2002)","DOI":"10.1109\/CEC.2002.1004493"},{"key":"47_CR27","doi-asserted-by":"crossref","unstructured":"Lawal, I.A., Abdulkarim, S.A., Hassan, M.K., Sadiq, J.M.: Improving HSDPA traffic forecasting using ensemble of neural networks. In: Proceedings of the IEEE International Conference on Machine Learning and Applications, pp. 308\u2013313 (2016)","DOI":"10.1109\/ICMLA.2016.0057"},{"key":"47_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-319-49409-8_7","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"C Lea","year":"2016","unstructured":"Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: a unified approach to action segmentation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 47\u201354. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_7"},{"issue":"1","key":"47_CR29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"1","author":"HB Mann","year":"1947","unstructured":"Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1(1), 50\u201360 (1947)","journal-title":"Ann. Math. Stat."},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"McLaughlin, N., Del Rincon, J.M., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1325\u20131334 (2016)","DOI":"10.1109\/CVPR.2016.148"},{"key":"47_CR31","unstructured":"Morrison, R.W.: Performance measurement in dynamic environments. In: Proceedings of the Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 5\u20138 (2003)"},{"key":"47_CR32","doi-asserted-by":"crossref","unstructured":"Neto, P.S.D.M., Petry, G.G., Aranildo, R.L., Ferreira, T.A.: Combining artificial neural network and particle swarm system for time series forecasting. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2230\u20132237 (2009)","DOI":"10.1109\/IJCNN.2009.5178926"},{"key":"47_CR33","doi-asserted-by":"crossref","unstructured":"Oldewage, E.T.: The perils of particle swarm optimisation in high dimensional problem spaces. MSc Thesis, University of Pretoria (South Africa) (2017)","DOI":"10.1109\/SSCI.2017.8280887"},{"key":"47_CR34","doi-asserted-by":"crossref","unstructured":"Pampar\u00e0, G., Engelbrecht, A.P.: Self-adaptive quantum particle swarm optimization for dynamic environments. In: Proceedings of the 11th International Conference on Swarm Intelligence, pp. 163\u2013175 (2018)","DOI":"10.1007\/978-3-030-00533-7_13"},{"key":"47_CR35","doi-asserted-by":"crossref","unstructured":"Poli, R., Broomhead, D.: Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 134\u2013141 (2007)","DOI":"10.1145\/1276958.1276977"},{"issue":"3","key":"47_CR36","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s11721-012-0071-6","volume":"6","author":"AS Rakitianskaia","year":"2012","unstructured":"Rakitianskaia, A.S., Engelbrecht, A.P.: Training feedforward neural networks with dynamic particle swarm optimisation. Swarm Intell. 6(3), 233\u2013270 (2012)","journal-title":"Swarm Intell."},{"key":"47_CR37","unstructured":"Riedmiller, M.: RPROP-Description and implementation details. Technical report. University of Karlsruhe, Karlsruhe (1994)"},{"key":"47_CR38","doi-asserted-by":"crossref","unstructured":"R\u00f6bel, A.: The dynamic pattern selection algorithm: effective training and controlled generalization of backpropagation neural networks. (Technical Report). Technische Universit\u00e4t Berlin (1994)","DOI":"10.1007\/978-1-4471-2097-1_151"},{"issue":"6","key":"47_CR39","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)","journal-title":"Psychol. Rev."},{"issue":"2","key":"47_CR40","first-page":"141","volume":"57","author":"J Sun","year":"2010","unstructured":"Sun, J., Fang, W., Xu, W.: A quantum-behaved particle swarm optimization with diversity-guided mutation for the design of two-dimensional IIR digital filters. IEEE Trans. Circ. Syst. II 57(2), 141\u2013145 (2010)","journal-title":"IEEE Trans. Circ. Syst. II"},{"issue":"2","key":"47_CR41","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.fcij.2018.10.003","volume":"3","author":"A Tealab","year":"2018","unstructured":"Tealab, A.: Time series forecasting using artificial neural networks methodologies: a systematic review. Future Comput. Inform. J. 3(2), 334\u2013340 (2018)","journal-title":"Future Comput. Inform. J."},{"issue":"26","key":"47_CR42","first-page":"84","volume":"2000","author":"F Van den Bergh","year":"2000","unstructured":"Van den Bergh, F., Engelbrecht, A.P.: Cooperative learning in neural networks using particle swarm optimizers. South Afr. Comput. J. 2000(26), 84\u201390 (2000)","journal-title":"South Afr. Comput. J."},{"issue":"1","key":"47_CR43","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1162\/neco.1989.1.1.39","volume":"1","author":"A Waibel","year":"1989","unstructured":"Waibel, A.: Modular construction of time-delay neural networks for speech recognition. Neural Comput. 1(1), 39\u201346 (1989)","journal-title":"Neural Comput."},{"issue":"6","key":"47_CR44","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1109\/72.165592","volume":"3","author":"LF Wessels","year":"1992","unstructured":"Wessels, L.F., Barnard, E.: Avoiding false local minima by proper initialization of connections. IEEE Trans. Neural Netw. 3(6), 899\u2013905 (1992)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"47_CR45","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1287\/mnsc.6.3.324","volume":"6","author":"PR Winters","year":"1960","unstructured":"Winters, P.R.: Forecasting sales by exponentially weighted moving averages. Manage. Sci. 6(3), 324\u2013342 (1960)","journal-title":"Manage. Sci."},{"issue":"1","key":"47_CR46","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35\u201362 (1998)","journal-title":"Int. J. Forecast."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02725-2_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:38:03Z","timestamp":1759279083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02725-2_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032027245","9783032027252"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02725-2_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"A Coru\u00f1a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}