Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin

Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). T...

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Bibliographic Details
Main Author:	Ahmad Kamaruddin, Saadi
Format:	Book Section
Language:	English
Published:	Institute of Graduate Studies, UiTM 2018
Subjects:	Analysis Instruments and machines
Online Access:	http://ir.uitm.edu.my/id/eprint/22104/ http://ir.uitm.edu.my/id/eprint/22104/1/ABS_SAADI%20AHMAD%20KAMARUDDIN%20TDRA%20VOL%2014%20IGS%2018.pdf

id	uitm-22104
recordtype	eprints
spelling	uitm-221042018-11-13T07:57:57Z http://ir.uitm.edu.my/id/eprint/22104/ Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin Ahmad Kamaruddin, Saadi Analysis Instruments and machines Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values. Therefore, the main objective of this research is to modify the backpropagation algorithm of nonlinear autoregressive (NAR) and autoregressive moving average (NARMA) models using Tukey-bisquare estimator and a proposed hybrid firefly algorithm on the least median of squares (FFA-LMedS), in order to manage outlying data efficiently, hence produce more accurate forecasted values. The proposed neural network models are named as modified NAR and NARMA models, which able to handle various degrees of outliers problem in time series data. The performance of the fitted neural network models are examined on both real and simulated datasets. The error measures to assess the performance are Root Mean Square Errors (RMSE), Mean Square Prediction Error (MSPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Geometric Root Mean Square Error (GRMSE)… Institute of Graduate Studies, UiTM 2018 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/22104/1/ABS_SAADI%20AHMAD%20KAMARUDDIN%20TDRA%20VOL%2014%20IGS%2018.pdf Ahmad Kamaruddin, Saadi (2018) Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin. In: The Doctoral Research Abstracts. IGS Biannual Publication, 14 . Institute of Graduate Studies, UiTM, Shah Alam.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Analysis Instruments and machines
spellingShingle	Analysis Instruments and machines Ahmad Kamaruddin, Saadi Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
description	Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values. Therefore, the main objective of this research is to modify the backpropagation algorithm of nonlinear autoregressive (NAR) and autoregressive moving average (NARMA) models using Tukey-bisquare estimator and a proposed hybrid firefly algorithm on the least median of squares (FFA-LMedS), in order to manage outlying data efficiently, hence produce more accurate forecasted values. The proposed neural network models are named as modified NAR and NARMA models, which able to handle various degrees of outliers problem in time series data. The performance of the fitted neural network models are examined on both real and simulated datasets. The error measures to assess the performance are Root Mean Square Errors (RMSE), Mean Square Prediction Error (MSPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Geometric Root Mean Square Error (GRMSE)…
format	Book Section
author	Ahmad Kamaruddin, Saadi
author_facet	Ahmad Kamaruddin, Saadi
author_sort	Ahmad Kamaruddin, Saadi
title	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_short	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_full	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_fullStr	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_full_unstemmed	Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin
title_sort	modified artificial neural network (ann) models for malaysian construction costs indices (mcci) data / saadi ahmad kamaruddin
publisher	Institute of Graduate Studies, UiTM
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/22104/ http://ir.uitm.edu.my/id/eprint/22104/1/ABS_SAADI%20AHMAD%20KAMARUDDIN%20TDRA%20VOL%2014%20IGS%2018.pdf
first_indexed	2023-09-18T23:07:56Z
last_indexed	2023-09-18T23:07:56Z
_version_	1777418608615358464

Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin

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