The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a re- sult, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better result outcome without the stationarity transformation.
Monthly data about oil production at several drilling wells is an example of
spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
- Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
models are proposed and applied for forecasting monthly oil production data at three
drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
parts, i.e. the first 50 observations for training data and the last 10 observations for
testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
models based on neural networks and GSTAR is needed for developing new
hybrid models that could improve the forecast accuracy.
Numerous routing protocols for mobile ad hoc networks (MANETs) have been designed in process information delivery from a source node to a destination node. In this paper, the Taguchi’s design of experiment (TDE) has been applied to investigate the performance of Destination Sequence Distance Vector (DSDV) routing protocol in MANETs. The effects of network parameters namely terrain sizes, node speeds, network sizes, transmission ranges, transmission rates, pause times and the number of maximum connections on packet delivery ratio and routing overhead in medium scale ad hoc networks have been done through simulation experiments. Through this study, we can rank these factors that may affect packet delivery ratio and routing overhead. The response performance was analyzed based on signal-to-noise ratio and analysis of variance (ANOVA). The results revealed that the transmission range was the most influential factor on the packet delivery ratio, followed by terrain size and transmission rate. The network size had the greatest effect on routing overhead, followed by the transmission range.
The air pollution index (API) has been recognized as one of the important air quality indicators used to record the
correlation between air pollution and human health. The API information can help government agencies, policy makers
and individuals to prepare precautionary measures in order to eliminate the impact of air pollution episodes. This study
aimed to verify the monthly API trends at three different stations in Malaysia; industrial, residential and sub-urban areas.
The data collected between the year 2000 and 2009 was analyzed based on time series forecasting. Both classical and
modern methods namely seasonal autoregressive integrated moving average (SARIMA) and fuzzy time series (FTS) were
employed. The model developed was scrutinized by means of statistical performance of root mean square error (RMSE).
The results showed a good performance of SARIMA in two urban stations with 16% and 19.6% which was more satisfactory
compared to FTS; however, FTS performed better in suburban station with 25.9% which was more pleasing compared
to SARIMA methods. This result proved that classical method is compatible with the advanced forecasting techniques in
providing better forecasting accuracy. Both classical and modern methods have the ability to investigate and forecast
the API trends in which can be considered as an effective decision-making process in air quality policy.
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
error graphically. From the result obtained this model gives a better forecast compare to the other two models.