

Articles
by
A. Shabri 
Total Records (
3 ) for
A. Shabri 





Z. Ismail
,
A. Yahya
and
A. Shabri


Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the meltdown of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted model of MLR will be used to predict the future gold prices. A naive model known as "forecast1" was considered to be a benchmark model in order to evaluate the performance of the model. Results: Many factors determine the price of gold and based on "a hunch of experts", several economic factors had been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (TBILL) and US Dollar index (USDX) were considered to have influence on the prices. Parameter estimations for the MLR were carried out using Statistical Packages for Social Science package (SPSS) with Mean Square Error (MSE) as the fitness function to determine the forecast accuracy. Conclusion: Two models were considered. The first model considered all possible independent variables. The model appeared to be useful for predicting the price of gold with 85.2% of sample variations in monthly gold prices explained by the model. The second model considered the following four independent variables the (CRB lagged one), (EUROUSD lagged one), (INF lagged two) and (M1 lagged two) to be significant. In terms of prediction, the second model achieved high level of predictive accuracy. The amount of variance explained was about 70% and the regression coefficients also provide a means of assessing the relative importance of individual variables in the overall prediction of gold price. 





A. Shabri
,
R. Samsudin
and
Z. Ismail


Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. In this study, a hybrid methodology that combines the individual forecasts based on artificial neural network (CANN) approach for modeling rice yields was investigated. The CANN has several advantages compared with conventional Artificial Neural Network (ANN) model, the statistical the autoregressive integrated moving average (ARIMA) and exponential smoothing (EXPS) model in order to get more effective evaluation. To assess the effectiveness of these models, we used 38 years of time series records for rice yield data in Malaysia from 1971 to 2008. Results show that the CANN model appears to perform reasonably well and hence can be applied to reallife prediction and modeling problems. 




R. Samsudin
,
A. Shabri
and
P. Saad


Time series prediction is an important problem in many applications in natural science, engineering and economics. The objective of this study is to examine the flexibility of Support Vector Machine (SVM) in time series forecasting by comparing it with a multilayer backpropagation (BP) neural network. Five wellknown time series data sets are used in this study to demonstrate the effectiveness of the forecasting model. These data are utilized to forecast through an application aimed to handle real life time series. The grid search technique using 10fold cross validation is used to determine the best value of SVM parameters in the forecasting process. The experiment shows that SVM outperforms the BP neural network based on the criteria of Mean Absolute Error (MAE). It also indicates that SVM provides a promising technique in time series forecasting techniques. 





