The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences. Dust storms have interannual variabilities and are typical disruptive events. The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when d...

This paper identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialised counties has noted the superior performance of the FIGARCH model in the case of industrialised countries, a result that is reaffirmed here. However, we show that when dealing with developing countries’ d...

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in...

This research analyzes how individuals make forecasts based on time series data, and tests an intervention designed to improve forecasting performance. Using data from a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters over-react to errors in relatively stable environments, but under-react to errors in relatively...

The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this trianglechr('39')s characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski tr...

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk based on theories of decision-making under uncertainty, which won the 2002 Nobel Prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of o...

This study examines the forecasting performance of Adaptive Neuro Fuzzy Inference System(ANFIS) compared in comparison to statistical autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) model in forecasting of rice yield production.. To assess the effectiveness of these models, we used 9 years of time series records for rice yield data in Malaysia from 1995 ...