Kolay, Erdinç2025-03-232025-03-2320192667-4211https://doi.org/10.18038/aubtda.443510https://search.trdizin.gov.tr/tr/yayin/detay/359222https://hdl.handle.net/11486/3676Time series prediction is a method to predict the system behavior in the future based on current given data. Neural Networks(NNs) approach is a well-known technique that is useful for time series prediction. In the literature many NN models such asMultilayer Perceptron (MLP), Pi-Sigma NN (PSNN), Recurrent NN etc. are proposed for solving time series prediction. In thispaper, we use Multiplicative Neuron Model (MNM) to predict time series. For training this model, we propose to use newlydeveloped evolutionary optimization algorithm called Sine Cosine algorithm (SCA), and this algorithm has not been used asfar as we know in training the MNM. The proposed SCA-MNM model is employed for the well-known time series problems.In this paper, the application of the SCA-MNM on time prediction is illustrated using two mostly used datasets Mackey-Glasstime series dataset, Box-Jenkins gas furnace dataset. To investigate the effect of the proposed SCA-MNM model, comparisonsare made with some of the results given in the literature.eninfo:eu-repo/semantics/openAccessMühendislikElektrik ve ElektronikBilgisayar BilimleriYazılım MühendisliğiBilgisayar BilimleriSibernitikBilgisayar BilimleriBilgi SistemleriBilgisayar BilimleriDonanım ve MimariBilgisayar BilimleriTeori ve MetotlarBilgisayar BilimleriYapay ZekaArticle20215316010.18038/aubtda.443510359222