Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors and that yet the resulting factors are less useful for forecasting, and the answer is yes. Such a ...

Large regional health care systems face challenges in planning physician schedules and appointments to achieve patient-access goals and efficient use of resources. We describe the development of a planning and forecasting system that predicts outpatient visits (OPVs) for 23 primary and specialty care clinics at a large medical center in Rochester, Minn. We develop and compare univariate, multiv...

In the Summer of 2008 we have upgraded STAMP to version 8.10 which is the current release version. The new items in version 8.10 are relatively small. The forecasting dialog allows the forecasting of the unobserved components as well as the future observations. More importantly, various errors have been removed from the program and some improvements have been introduced. Most notably, the batch...

Conventional GM(1, 1|α) prediction always produces the huge singleton residual error around the turning point region of a time series and this phenomenon is called overshooting. A novel forecasting technique using a hybrid BPNN-weighted Grey-CLSP (BWGC) prediction that employs a back-propagation neural net (BPNN) to automatically adjust a linear combination of GM(1, 1|α) prediction and the cumu...

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influe...

A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provi...

This paper demonstrates the framework and methodology how weed population dynamics can be predicted using rule-base fuzzy logic as applied to GIS spatial image. Parthenium weed (parthenium hysterophorus L.) infestation in the Central Queensland region poses a serious threat to the environment and economic viability of the infected areas. Government agencies have taken steps to control and manag...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...

Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global f...