The enhanced BPNN-NAR and BPNN-NARMA models for Malaysian aggregate cost indices with outlying data
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....
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2015
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/50812/ http://irep.iium.edu.my/50812/ http://irep.iium.edu.my/50812/ http://irep.iium.edu.my/50812/1/50812.pdf http://irep.iium.edu.my/50812/2/50812-The%20enhanced%20BPNN-NAR%20and%20BPNN-NARMA%20models%20for%20Malaysian%20aggregate%20cost%20indices%20with%20outlying%20data_SCOPUS.pdf |
| Summary: | 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. |
|---|