Bakhary, Norhisham and Yahya, Khairulzan and Ng Chin, Nam (2004) Univariate Artificial Neural Network In Forcasting Demand Of Low Cost House In Petaling Jaya. Jurnal Teknologi B (40B). pp. 67-75. ISSN 0127-9696
Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to April 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880 %. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record.
|Uncontrolled Keywords:||Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE|
|Subjects:||T Technology > TA Engineering (General). Civil engineering (General)|
|Deposited By:||En Mohd. Nazir Md. Basri|
|Deposited On:||07 Mar 2007 07:03|
|Last Modified:||21 Mar 2017 07:12|
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