Universiti Teknologi Malaysia Institutional Repository

Time series modeling and designing of artifical neural network (ANN) for revenue forecasting

Mohd. Yusof, Norfadzlia (2005) Time series modeling and designing of artifical neural network (ANN) for revenue forecasting. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.

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Abstract

Artificial neural networks (ANN) have found increasing consideration in forecasting theory. However, the large numbers of parameters that must be selected to develop ANN forecasting model have meant that the design process still involves much trial and error. The objective of this study is to investigate the effect of applying different number of input nodes, activation functions and pre-processing techniques on the performance of backpropagation (BP) network in time series revenue forecasting. In this study, several pre-processing techniques are presented to remove the non-stationary in the time series and their effect on ANN model learning and forecast performance are analyzed. Trial and error approach is used to find the sufficient number of input nodes as well as their corresponding number of hidden nodes which obtain using Kolmogorov theorem. This study compares the used of logarithmic function and new proposed ANN model which combines sigmoid function in hidden layer and logarithmic function in output layer, with the standard sigmoid function as the activation function in the nodes. A cross-validation experiment is employed to improve the generalization ability of ANN model. From the empirical findings, it shows that an ANN model which consists of small number of input nodes and smaller corresponding network structure produces accurate forecast result although it suffers from slow convergence. Sigmoid activation function decreases the complexity of ANN and generates fastest convergence and good forecast ability in most cases in this study. This study also shows that the forecasting performance of ANN model can considerably improve by selecting an appropriate pre-processing technique.

Item Type:Thesis (Masters)
Additional Information:Thesis (Master of Science (Computer Science)) - Universiti Teknologi Malaysia, 2006; Supervisor : Assoc. Prof. Dr. Siti Mariyam Binti Shamsuddin
Uncontrolled Keywords:artificial neural networks (ANN), forecasting performance, pre-processing technique, backpropagation network, time series revenue forecasting
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:4799
Deposited By: Ms Zalinda Shuratman
Deposited On:03 Mar 2008 00:56
Last Modified:03 Aug 2012 00:42

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