Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.
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.
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.
Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a) improves our understanding on the variability and productivity of the marine environment, which is important for exploring fishery resources. Monthly level 3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the archipelagic waters of Spermonde Indonesia were used to investigate the relationship between SST and chl-a and to forecast the potential fishing ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive correlation was also found between SST and chl-a with the catch of R. kanagurta (R=0.7, p<0.05). The potential fishing grounds of R. kanagurta were found located along the coast (at accuracy of 76.9%). This study indicated that, with the integration of remote sensing technology, statistical modeling and geographic information systems (GIS) technique were able to determine the relationship between SST and chl-a and also able to forecast aggregation of R. kanagurta. This may contribute in decision making and reducing search hunting time and cost in fishing activities.
Coxmodel is popular in survival analysis. In the case of time-varying covariate;
several subject-specific attributes possibly to change more frequently than others. This
paper deals with that issue. This study aims to analyze survival data with time-varying
covariate using a time-dependent covariate Cox model. The two case studies employed in
this work are (1) delisting time of companies from IDX and (2) delisting time of company
from LQ45 (liquidity index). The survival time is the time until a company is delisted
from IDX or LQ45. The determinants are eighteen quarterly financial ratios and two
macroeconomics indicators, i.e., the Jakarta Composite Index (JCI) and BI interest rate
that changes more frequent. The empirical results show that JCI is significant for both
delisting and liquidity whereas BI rate is significant only for liquidity. The significant
firm-specific financial ratios vary for delisting and liquidity.
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.