Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Authors:
Coşkun Hamzaçebi;Diyar Akay;Fevzi Kutay
Affiliations:
Z. Karaelmas University, Department of Business and Administration, Incivez, 67100 Zonguldak, Turkey;Gazi University, Department of Industrial Engineering, Ankara, Turkey;Gazi University, Department of Industrial Engineering, Ankara, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multi-periodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result.