In developing a time series model, parameter estimation is one of the crucial steps. Common methods of estimation include method of moment (MME), ordinary least square estimation (OLS) and maximum likelihood estimation (MLE). The purpose of the current study is to model and forecast the prices of Malaysian gold called kijang emas using Box-Jenkins methodology. To find the best model, parameter estimates using OLS and MLE were computed. Based on the Akaike information criteria (AIC) and mean absolute percentage error (MAPE), the model estimated with OLS was found to perform better.

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Market properties and shares are important in the field of finance in order to measure the economic growth of a country. These market properties are volatile time series as they have huge price swings in a shortage or an oversupply period. In this study, we use two time series models which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heterocedasticity (GARCH) models in modelling and forecasting Malaysia property market. The capabilities of ARIMA and GARCH models in modelling and forecasting Malaysia property market will be evaluated by using Akaike's Information Criterion (AIC), Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). It can be concluded that Box-Jenkins ARIMA model perform better compared than GARCH model in modelling and forecasting Malaysia market properties and shares. 7002Nor Hamizah Miswan et al.

An effective way to improve forecast accuracy is to use a hybrid model. This paper proposes a hybrid model of linear autoregressive moving average (ARIMA) and non-linear GJR-GARCH model also known as TARCH in modeling and forecasting Malaysian gold. The goodness of fit of the model is measured using Akaike information criteria (AIC) while the forecasting performance is assessed using mean absolute percentage error (MAPE), bias proportion, variance proportion and covariance proportion.

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.

A hybrid model has been considered an effective way to improve forecast accuracy. This paper proposes the hybrid model of the linear autoregressive moving average (ARIMA) and the non-linear generalized autoregressive conditional heteroscedasticity (GARCH) in modeling and forecasting. Malaysian gold price is used to present the development of the hybrid model. The goodness of fit of the model is measured using Akaike information criteria (AIC) while the forecasting performance is assessed using bias, variance proportion, covariance proportion and mean absolute percentage error (MAPE).

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As a developing country, Malaysia is expected an increment on their electricity consumption align with growing of economy, population as well as industrial demands. For that reason, this paper brings an analysis that considered some macro factors identified as geographical parameters, meteorological parameters, and economic parameters which believed will effect the demand in electricity over the country. However, the case study for this research is focusing the demands in the area named Johor Bahru and Skudai due to the higher electricity consumption in Johor, Malaysia. The correlation coefficient is introduced as a tool to measure the significant factors influenced the demands. From the result obtained, it shown that Gross Domestic Product (GDP), population and maximum temperature were affected the electrical load demand pattern based on their Pearson Correlations. As an advantages this finding will help others researcher and electrical utilities in forecasting their future demands.