A New Neural Network Model with Deterministic Trend and Seasonality Components for Time Series Forecasting
Pp. 76-92 (17)
Erol Egrioglu, Cagdas Hakan Aladag, Ufuk Yolcu, Eren Bas and Ali Z. Dalar
Artificial neural networks have been commonly used for time series
forecasting problem in the last years. When they are compared with classical time
series methods, artificial neural networks have some advantages. Artificial neural
networks do not need any assumption such as normality and linearity. In recent years,
different types of artificial neural networks have been proposed for time series
forecasting. In these networks, the inputs are lagged variables or other time series. It is
well known that some time series have deterministic trend and this kind of time series
should be modeled by using different functions of time (t) as inputs. In the modeling
such type time series, using only lagged variables will lead to insufficient results. In
this study, a new neural network model that has different functions of time as inputs is
proposed for solving this problem. The proposed method is compared with other
methods in the literature according to forecast performance. It is obtained that the new
model outperforms other methods.
Artificial neural networks, Forecasting, Particle swarm optimization,
Seasonality, Time series.
Department of Statistics, Faculty of Arts and Sciences, Giresun University, Giresun, Turkey.