Results of Fitted Neural Network Models on Malaysian Aggregate Dataset

Bulletin of Electrical Engineering and Informatics
Vol. 7, No. 2, June 2018, pp. 272~278
ISSN: 2302-9285, DOI: 10.11591/eei.v7i2.1177  272
Journal homepage: http://journal.portalgaruda.org/index.php/EEI/index
Results of Fitted Neural Network Models on Malaysian
Aggregate Dataset
Nor Azura Md Ghani1
, Saadi Bin Ahmad Kamaruddin2
, Ismail Musirin3
, Hishamuddin Hashim4
1,2
Center for Statistical Studies and Decision Sciences, Faculty of Computer and Mathematical Sciences, Universiti
Teknologi MARA,40450 Shah Alam, Selangor, Malaysia
3
Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor Darul Ehsan, Malaysia
4
Pilihsepakat Sdn. Bhd., Lot 5431, Tingkat 1(B), Jalan J9, Fasa 6, Taman Melawati, Kuala Lumpur, Malaysia
Article Info ABSTRACT
Article history:
Received Jan 25, 2018
Revised Mar 26, 2018
Accepted Apr 09, 2018
This result-based paper presents the best results of both fitted BPNN-NAR
and BPNN-NARMA on MCCI Aggregate dataset with respect to different
error measures. This section discusses on the results in terms of the
performance of the fitted forecasting models by each set of input lags and
error lags used, the performance of the fitted forecasting models by the
different hidden nodes used, the performance of the fitted forecasting models
when combining both inputs and hidden nodes, the consistency of error
measures used for the fitted forecasting models, as well as the overall best
fitted forecasting models for Malaysian aggregate cost indices dataset.
Keywords:
Aggregate
Backpropagation
Neural network
Ordinary Nonlinear
Autoregressive (NAR)
Ordinary Nonlinear
Autoregressive Moving
Average (NARMA)
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Nor Azura Md Ghani,
Center for Statistical Studies and Decision Sciences,
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA,
40450 Shah Alam, Selangor, Malaysia.
Email: azura@tmsk.uitm.edu.my
1. INTRODUCTION
According to [1], input lags alone may not be adequate to precisely rough the underlying generation
process. He added that one lag is not able to capture the underlying relationship or not sufficient to model a
process, thus there is a need of adequate number of hidden neurons for optimal network performance. Both
inputs and hidden nodes can significantly affect the learning of a neural network when forecasting a time
series. This research is interested in accuracy of the models instead of parsimonious time series models, as
suggested by [2]. For time series, inputs also include lags. As the input dimensionality increases, model
complexity increases and learning becomes more difficult, leading to poor convergence. The challenge here
is to select or find the best combinations of input lags with adequate number of hidden nodes that will lead to
a superior model, specifically to a particular dataset of a certain scenario. If the training set is sufficiently
large, ANN will generalise accurately and will produce accurate outputs for inputs not in the training set.
When neural networks are properly trained, they can generalise and extrapolate additional details of the
function mapping the inputs to outputs. Nevertheless, no prior assumptions of dependent and independent
variables need to be made since the neural network is trained on observed data [3], [4]. This infers neural
systems are ordinarily used as "black-box" devices that are no earlier information about the method was
assumed; the goal was to develop a process model based only on observations of its input-output behaviour

BEEI ISSN: 2302-9285 
Results of Fitted Neural Network Models on Malaysian Aggregate Dataset (Nor Azura Md Ghani1)
273
[5-7]. By definition, the black-box is a modelling technique that constructs the model just based on the
information gained from the framework, and it doesn't depend on upon other information about the system
[8]. No early presumptions about the model structure are made, and rather, the modelling technique’s concern
is to make a generic model that maps the information yield relationship of the dataset [9, 10]. Revelation
models are known to be viable and versatile, as the assistant information about the system may not be
promptly accessible. From this investigation, one shortcoming lies in the way that that the model complexity
expands [7] Also, another favorable position of the black-box identification is that it can demonstrate flow
that are inadequately caught by mathematical models [12], [13].
2. METHODOLOGY
In this research, Malaysian Aggregate monthly data from January 1980 to December 2013 were
adapted. These datasets are secondary data collected from three different sources, UKAS, from Malaysian
Prime Minister’s Office, Malaysian CIDB, and Malaysian Statistical Department. In this research, only
Malaysian central region aggregate price indices were adapted which comprise of 3.9% outliers. The total
N=408 (12 months x 34 years) from January 1980 to December 2013 (base 1980=100). The mean of
Malaysian Aggregate is 113.7731. Based on the Jarque-Bera test for normality, the variable aggregate (J-
B=0.873, p=0.000) is highly significant at 99% confidence interval.
First and foremost, this research discusses the performance of the fitted forecasting models on
Malaysian aggregate cost indices data by each set of input lags and error lags used. Table 1 shows
performance results of fitted forecasting models of two-layer tansig/linear transfer functions on Malaysian
aggregate cost indices data. For comparison purposes, this research used input lags from 5 to 40 and hidden
nodes from 5 to 45 for BPNN-NAR model, while for the BPNN-NARMA model, this research used the error
lags ranging from 5 to 40, together with input lags from 5 to 40 and hidden nodes from 5 to 45. The most
common approach to decide this property is experimentation or trial-and-error [14-16], albeit different
strategies (rule-of-thumb) and algorithms (pruning and growing) are additionally accessible [17]-[19]. It is to
be noticed that BPNN-NAR is a feedforward neural network type model, while BPNN-NARMA is a
recurrent neural network type model [18]. This exploration reports the error measures on the test dataset,
which is the most vital characteristic, reflecting ANN’s generalisation ability [19], [20]. Table 2 shows the
data partitioning for network pre-processing. Figure 1 shows the flowchart of the BPNN-NAR and BPNN-
NARMA.
Table 1. Combinations of Input Lags, Error Lags and Percentage of Outliers for BPNN - NAR and BPNN -
NARMA Models
No. Data Type Notation Outliers
ANN Models
Hidden
Nodes
NAR NARMA
Input Lags Input Lags Error Lags
1. Aggregate Agg 3.9%
5,
10,
15,
20,
25,
30,
35,
40
5,
10,
15,
20,
25,
30,
35,
40
5,
10,
15,
20,
25,
30,
35,
40
5,
10,
15,
20,
25,
30,
35,
40,
45
Table 2. Size of Data Partitioning for Training, Validation and Testing Sets for Each Data Used in This
Research
No. Data Type
Total Sample
Size (N)
In-sample Data Out-of-sample Data
Training (70%) Validation (15%) Testing (15%)
1.
Monthly Malaysian Aggregate
Price Indices Data
408 286 61 61

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Figure 1. Flowchart of Backpropagation Neural Network NAR and NARMA Mechanism
3. RESEARCH FINDINGS
Figure 2 shows the performance of BPNN-NAR with respect to input lags and hidden nodes on
Malaysian aggregate cost indices data based on RMSE. Whereas, Figure 3 shows the performance of BPNN-
NARMA with respect to input lags and hidden nodes on Malaysian aggregate cost indices data based on

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275
RMSE. From the radar diagrams of both figures, this research can clearly see that overall of the lines of
BPNN-NAR are more towards the center of zero values, meaning zero error compared to BPNN-NARMA.
Figure 2. Performance of BPNN-NAR with respect to input lags and hidden nodes on Malaysian Aggregate
cost indices data based on RMSE

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Figure 3. Performance of BPNN-NARMA with respect to input lags, error lags and hidden nodes on
Malaysian aggregate cost indices data based on RMSE
Based on input and error lags 5, the optimal number of hidden nodes was 20. Based on input and
error lags 10, the optimal number of hidden nodes was 20. Based on input and error lags 15, the optimal
number of hidden nodes was 15. Based on input and error lags 20, the optimal number of hidden nodes was
20. Based on input and error lags 25, the optimal number of hidden nodes was 25. Based on input and error
lags 30, the optimal number of hidden nodes was 30.
Based on these results, BPNN-NARMA model performed worse than the BPNN-NAR model. By
right, BPNN-NARMA should perform better than BPNN-NAR [21]-[23]. This is perhaps due to the outliers
in the data. In most cases, as the number of input and error lags increased, the errors are also increased.
Furthermore, this research discusses on the performance of the fitted forecasting models by different
hidden nodes used. From Table 1, over the input and error lags, if the network is assigned with fewer nodes
or huge load of nodes, the performance of the network will get worse. This happens due to the network
overfitting problem [24]-[28]. Therefore, an adequate number of hidden nodes is important in neural network
modelling [29]-[31]. It can be concluded that if the network is given an adequate number of hidden nodes, the
performance of the network will get better and model errors will be reduced, and the chance of getting a more
accurate model can be achieved.
In terms of the consistency of error measures used for the fitted forecasting models, this research
concludes that RMSE performed consistently. This is supported by recent works in the forecasting arena such
as [32-36]. Moreover, in most cases, many researchers used RMSE to evaluate their forecasting models such
as [37, 38]. Therefore, RMSE will be the used to assess the performance of the models throughout this
research. The best results for this section based on RMSE can be summarized in Table 2.
Lastly, it can be concluded that the best fitted model for Malaysian aggregate cost indices data was
the two-layer tansig/linear transfer functions BPNN-NAR model with 10-20 configurations (RMSE=0.584,
MSPE=0.341, MAPE=55.378, MAD=0.429, and GRMSE=0.782), can be seen in Table 3.

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Table 3. Best Results of Ordinary BPNN-NAR and BPNN-NARMA Models on Malaysian Aggregate Cost
Indices Data based on Different Lags
Input
Lags
Error
Lags
Hidden
Nodes
RMSE
BPNN-NAR BPNN- NARMA
5 5 20 0.857 1.036
10 10 20 0.584 0.626
15 15 15 0.744 0.808
20 20 20 0.766 0.850
25 25 25 0.807 0.892
30 30 30 0.834 0.931
35 35 35 0.843 0.950
40 40 45 0.882 1.003
4. CONCLUSION
The investigation of BPNN-NAR on MCCI data demonstrates that higher input lags implies higher
RMSE. Correspondingly, the higher or the lesser hidden nodes to the input lags, the higher the RMSE, and
the higher the input lags, the higher the RMSE.
Despite the fact that NARMA is superior to NAR, when there are outliers in the dataset, the
NARMA display appeared to separate, and the NAR demonstrate outflanked the NARMA show. Here, this
examination proposes that the NAR model ought not be amplified or gone before by NARMA demonstrate
when the dataset comprises of outliers.
The NARMA shows performed more terrible than NAR when there exist outliers in the time
arrangement datasets. NARMA model should perform better contrasted with NARMA demonstrate. This can
be demonstrated by the aftereffects of both models when fitting Malaysian sand cost indices data, which don't
comprise of outliers issue. From the results, it is clear that that there is a need to modify NAR and NARMA
models so that they may handle outliers issue effectively, or else the expansion of NAR model to NARMA
model ought not be continued since the NARMA model will tend deliver bigger errors, and the outcomes are
not solid for further utilize. In the future, the results will be compared to the existing works of [39], [40].
ACKNOWLEDGEMENT
We want to express our gratefulness and appreciation to Unit Kerjasama Awam Swasta (UKAS)
over Prime Minister's Office, Construction Industry Development Board (CIDB) then Malaysian Statistics
Department. Appreciation to Universiti Teknologi MARA for financial support under the Grant Scheme No.
600-RMI/DANA 5/3/CIFI (65/2013).
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