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The improved BPNN-NAR and BPNN-NARMA models on malaysian aggregate cost indices with outlying data
Saadi Ahmad Kamaruddin1, Nor Azura Md Ghani2, Norazan Mohamed Ramli3.
Neurocomputing have been adapted in time series forecasting arena, but the
presence of outliers that usually occur in data time series may be harmful to the
data network training. This is because the ability to automatically find out any
patterns without prior assumptions and loss of generality. In theory, the most
common training algorithm for Backpropagation algorithms leans on reducing
ordinary least squares estimator (OLS) or more specifically, the mean squared error
(MSE). However, this algorithm is not fully robust when outliers exist in training data,
and it will lead to false forecast future value. Therefore, in this paper, we present a
new algorithm that manipulate algorithms firefly on least median squares
estimator (FFA-LMedS) for Backpropagation neural network nonlinear
autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear
autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in
time series data. The performances of the proposed enhanced models with
comparison to the existing enhanced models using M-estimators, Iterative LMedS
(ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done
based on root mean squared error (RMSE) values which is the main highlight of this
paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate
cost indices data set from January 1980 to December 2012 (base year 1980=100)
with different degree of outliers problem is adapted in this research. At the end of
this paper, it was found that the enhanced BPNN-NARMA models using Mestimators,
ILMedS and FFA-LMedS performed very well with RMSE values almost
zero errors. It is expected that the findings would assist the respected authorities
involve in Malaysian construction projects to overcome cost overruns.
Affiliation:
- International Islamic University Malaysia, Malaysia
- Universiti Teknologi MARA, Malaysia
- Universiti Teknologi MARA, Malaysia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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