Advances in Time Series Forecasting

Advances in Time Series Forecasting

Volume: 2

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting ...
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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

Abstract

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.

Keywords:

Artificial neural networks, Forecasting, Particle swarm optimization, Seasonality, Time series.

Affiliation:

Department of Statistics, Faculty of Arts and Sciences, Giresun University, Giresun, Turkey.