In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.
 M. Basu, T.K. Ho, Learning behavior of single neuron classifiers onlinearly separable or nonseparable inputs, In IEEE LICNN’99, 1999.
 R. Labib, New single neuron structure for solving non-linear problems, In IEEE IJCNN’99, 1999, 617–620.
 T.A. Plate, Randomly connected sigma–pi neurons can form associator networks, NETCNS: Network: Computation in Neural Systems, 11, 2000, 321–322.
 C.N. Zhang, M. Zhao, M. Wang, Logic operations based on single neuron rational model, IEEE Transactions on Neural Networks, 11, 2000, 739–747.
 R.N. Yadav, N. Kumar, P.K. Kalra, J. John, Learning with generalized-mean neuron model. Neurocomputing, 69, 2006, 2026-2032.
 M. Shiblee, B. Chandra, P.K. Kalra, Learning of geometric mean neuron model using resilient propagation algorithm, Expert Systems with Applications, 37, 2010, 7449-7455.
 C.H. Aladag, E. Egrioglu, U. Yolcu, Forecast combination by using artificial neural networks, Neural Processing Letters, 32 (3), 2010, 269–276.
 G. Zhang, B.E. Patuwo, Y.M. Hu, Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 1998, 35-62.
 R. Sharda, Neural networks for the MS/OR analyst: An application bibliography, Interfaces, 24 (2), 1994, 116–130.
 A.S. Weigend, B.A. Huberman, D.E. Rumelhart, Predict- ing the future: A connectionist approach, International Journal of Neural Systems, 1, 1990, 193–209.
 A.S. Weigend, B.A. Huberman, D.E. Rumelhart, Predict- ing sunspots and exchange rates with connectionist networks. In: Casdagli, M., Eubank, S. (Eds.), Nonlinear Modeling and Forecasting. Addison-Wesley, Redwood City, CA, 1992, 395–432
 M. Cottrell, B. Girard, Y. Girard, M. Mangeas, C. Muller, Neural modeling for time series: a statistical stepwise method for weight elimination, IEEE Transactions on Neural Networks, 6(6), 1995, 1355–1364.
 R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing, 7, 2007, 1157-1163.
 L. Zhao, Y. Yang, PSO-based single multiplicative neuron model for time series prediction, Expert Systems with Applications, 36, 2009, 2805-2812.
 C.H. Aladag, E. Egrioglu, U. Yolcu, A.Z. Dalar A new time invariant fuzzy time series forecasting method based on particle swarm optimization, Applied Soft Computing, 12, 2012, 3291-3299.
 R. Storn, K. Price, Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Technical Report TR-95-012, International Computer Science Institute, Berkeley, 1995.
 G. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 200, 159-175.
 C.H. Aladag, E. Egrioglu, C. Kadilar, Forecasting nonlinear time series with a hybrid methodology, Applied Mathematic Letters, 22, 2009, 1467-1470.
 U. Yolcu, E. Egrioglu, C.H. Aladag, A new linear & nonlinear artificial neural network model for time series forecasting, Decision Support Systems, 2013, 1340–1347.