Research ArticleOpen accessIn the keywordsModelling the Error Term of Australia Gross Domestic ProductAyodele Abraham Agboluaje, Suzilah Ismail, Chee Yin YipThe main aim of this study is to model the Gross Domestic Product (GDP) with the new Combine White Noise (CWN) Model and compare the results with the Vector Autoregressive (VAR) Model and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) Model which are the existing models. The CWN model estimation yields best results with least information criteria and high log likelihood … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 2 Nov 2016 · 6,930 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFuzzy Time Series: An Application to Tourism Demand ForecastingMuhammad Hisyam Lee, Maria Elena Nor, Suhartono et al.Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site … …American Journal of Applied Sciences, Vol. 9, Issue 1 · 24 Nov 2011 · 8,474 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting Peak Load Electricity Demand Using Statistics and Rule Based ApproachZ. Ismail, A. Yahya, K. A. MahpolProblem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as … …American Journal of Applied Sciences, Vol. 6, Issue 8 · 31 Aug 2009 · 7,936 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting Gold Prices Using Multiple Linear Regression MethodZ. Ismail, A. Yahya, A. ShabriProblem 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 … …American Journal of Applied Sciences, Vol. 6, Issue 8 · 31 Aug 2009 · 11,091 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNew Car Demand Modeling and Forecasting Using Bass Diffusion ModelZuhaimy Ismail, Noratikah AbuForecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented … …American Journal of Applied Sciences, Vol. 10, Issue 6 · 3 Jun 2013 · 7,332 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA STUDY ON NEW PRODUCT DEMAND FORECASTING BASED ON BASS DIFFUSION MODELZuhaimy Ismail, Noratikah AbuA forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. This study considers the Bass Model for forecasting the diffusion of new products or an … …Journal of Mathematics and Statistics, Vol. 9, Issue 2 · 29 Apr 2013 · 7,807 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNeural Network in Modeling Malaysian Oil Palm YieldZuhaimy Ismail, Azme KhamisProblem statement: Forecasting of palm oil yield has become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN). The main issue in yield forecasting is to … …American Journal of Applied Sciences, Vol. 8, Issue 8 · 19 Jul 2011 · 8,576 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Machine Learning Approach to Predictive Analytics in Precision Agriculture for Sustainable Crop OptimizationJignesh Kariya, Pankaj MudholkarDriven by data-enabled technologies, precision agriculture is evolving into an innovative way to increase agricultural productivity and sustainability. Conventional agricultural practices face several challenges, such as poor soil quality, inappropriate crop selection, improper resource utilization, and unpredictable climate conditions. To address these limitations, this paper proposes a hybrid machine learning framework for sustainable crop optimization and predictive analysis in precision … …Journal of Computer Science, Vol. 22, Issue 10 · 10 Oct 2026 · 865 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnchoring Public Health Forecasts: High-Precision Obesity Prevalence Estimation Via Hybrid Geodemographic Ensemble RegressionTrenton Ward, Isaac OsunmakindeObesity remains a major driver of chronic disease and rising healthcare costs in the United States. Yet, most existing predictive models rely on single-model classifiers and rarely incorporate spatial structure or ensemble-based regression, thereby limiting their ability to capture geodemographic variation. This study introduces a hybrid unsupervised–supervised ensemble framework that integrates K-Means spatial profiling with a Stacking Regressor to address … …Journal of Computer Science, Vol. 22, Issue 7 · 31 Jul 2026 · 632 accessesView PDFCite
Research ArticleOpen accessIn the abstractAI-Integrated Probabilistic Optimization for Inventory Control Under Stochastic DemandJanardan BeheraThe identification and operationalization of increasingly nonstationary and volatile demand patterns challenge the conduct of inventory management in contemporary supply chains. The classical models, especially EOQ and static policies, rest on the stability of demand assumptions, either deterministically or distributionally, and are hence less apt under real-world uncertainty. In view of these limitations, the present study puts forward an integrated … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 6 May 2026 · 2,217 accessesView PDFCite