Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

The hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation (ETKF– 3DVAR) system developed for the Weather Research and Forecasting (WRF) Model was further tested with real observations, as a follow-up for the observation system simulation experiment (OSSE) conducted in Part I. A domain encompassing North America was considered. Because of limited computational r...

An observing system simulation experiment (OSSE) has been carried out to evaluate the 1 impact of a hybrid ensemble-variational data assimilation algorithm for use with the National 2 Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE 3 provides a controlled framework for evaluating analysis and forecast errors since a truth is 4 known. In this case, the nature...

An enhanced version of the hybrid ensemble–three-dimensional variational data assimilation (3DVAR) system for the Weather Research and Forecasting Model (WRF) is applied to the assimilation of radial velocity (Vr) data from two coastal Weather Surveillance Radar-1988 Doppler (WSR-88D) radars for the prediction of Hurricane Ike (2008) before and during its landfall. In this hybrid system, flow-d...

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on ...

This paper develops an estimated hybrid model that combines the micro-founded DSGE model with the flexibility of the theoretical VAR model. The model is estimated via the maximum likelihood technique based on quarterly data on real Gross National Product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive esti...

this research studies the application of hybrid algorithms for predicting the prices of crude oil. brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. the proposed model was able to predict future crude oil prices from august...

High-resolution rainfall forecasting— the ability to predict rainfall intensities at enhanced resolution in space and time—is one of the most difficult, but also most useful, problems in applied meteorology and hydrology. Such forecasting could help scientists predict floods and manage water resource operations. Despite its well-known benefits,1 however, scientists have made little progress ove...

An ensemble Kalman filter–variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winte...