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At least 19 recordsLinked to original sources
PubMed2005

[A connection number-based principal factor analysis forecast method to forecast the encephalitis B epidemics].

OBJECTIVE: To detect the relations between incidence rate of the epidemical encephalitis B and related factors, to provide a simple, valid and practical new method for forecasting encephalitis B eipdemics. METHODS: Connection number between the incidence rate of encephalitis B and the historical forecast factors was computed, before ranking the first, second and the third principal factor, to remove the factor with the smallest value in the light of the connection number before comparing the newest value of forecast factors with the same kind of history while the most nearly value becoming the forecasting factor value and to establish a forecasting equation according to the factor value and the consistent degree of the incidence rate of encephalitis B at that time. Finally, to put into the new factor value to get this forecast value under this equation. Assuming that there are n' (n' >or= 2) forecast factors, this time forecast value can then be directly obtained from the average of these estimate values. RESULTS: Using above forecast method to forecast the incidence rate of encephalitis B at certain place and year, the predicting value is very much close to the actual incidence rate. Difference between the predicting value forecasted by the above-mentioned method and the actual incidence rate is only 0.0264/100 000 with an accurate rate of 97.94%. CONCLUSION: This principal factor analysis forecast method based on connection number in forecasting the incidence rate of encephalitis B prevention is acceptable.

China↗
PubMed1991

An empirical analysis of the effect of length of forecast horizon on population forecast errors.

Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States. We find that in most instances there is a linear or nearly linear relationship between forecast accuracy and the length of the forecast horizon, but no consistent relationship between bias and the length of the horizon. We believe that these results provide useful information regarding the nature of population forecast errors.

Humans↗
PubMed2003

Forecasting peak daily ozone levels: part 2--A regression with time series errors model having a principal component trigger to forecast 1999 and 2002 ozone levels.

A modified time series approach, a Box-Jenkins regression with time series errors (RTSE) model plus a principal component (PC) trigger, has been developed to forecast peak daily 1-hr ozone (O3) in real time at the University of Wisconsin-Milwaukee North (UWM-N) during 1999 and 2002. The PC trigger acts as a predictor variable in the RTSE model. It tries to answer the question: will the next day be a possible high O3 day? To answer this question, three PC trigger rules were developed: (1) Hi-Low Checklist, (2) Discriminant Function Approach I, and (3) Discriminant Function Approach II. Also, a pure RTSE model without including the PC trigger and a persistence approach were tested for comparison. The RTSE model with DFA I successfully forecast the only two 1-hr federal exceedances (124 ppb), one in 1999 and one in 2002. In terms of the O3 100-ppb exceedances, 60-80% of the incorrect forecasts occurred with incorrect PC decisions. A few others may have been caused by unexpected O3-weather relations. When the three approaches used UWM-N data to forecast a 100-ppb exceedance somewhere in the Milwaukee, WI, metropolitan area, their performance was dramatically improved: the false alarm rate was reduced from 0.89 to 0.44, and the probability of detection was increased from 0.71 to 0.88.

Forecasting↗
PubMedDate not supplied

Forecasting the economic future of medical care ... and, forecasting for your department.

The growth of health-care spending in the United States depends more upon macroeconomic changes in personal income and inflation than upon changes in health-care reimbursement or regulations. Good forecasts for the next 4 years can be made using information available today. The outlook is for continued real growth of about 5% annually in the health sector, concentrated in the emerging services least subject to reimbursement controls: home health, diagnostics, long-term care, and pharmaceuticals. The long-run forecast is more speculative. International comparisons suggest that the United States will adjust spending imbalances that contribute to the federal deficit by reducing the rate of increase. Health expenditures will continue to grow, rising from the current 11% of gross national product (GNP) to 15% over the next 20 years, and are likely to exceed +15,000 per person by 2010. The article concludes by moving from a national and global perspective to focus on how to make forecasts of volume and costs within your department. Analysis of consistency and rate of change are the keys to successful and objective forecasting.

Delivery of Health Care↗
PubMed1991

The impact of forecasting methodology on the accuracy of national population forecasts: evidence from the Netherlands and Czechoslovakia.

"This study considers the accuracy of national population forecasts of the Netherlands and the Czechoslovak Socialist Republic.... We look at the demographic components employed in each forecast, the procedure to extrapolate fertility and the level at which assumptions for each component are formulated. Errors in total population size, fertility, mortality and foreign migration, and age structure are considered. We discuss trends in errors and methodology since 1950 and compare the situations in the two countries. The findings suggest that methodology has only a very limited impact on the accuracy of national population forecasts."

Czechoslovakia↗
PubMedDate not supplied

Operational monitoring and forecasting in the Aegean Sea: system limitations and forecasting skill evaluation.

The POSEIDON system, based on a network of 11 oceanographic buoys and a system of atmospheric/oceanic models, provides real-time observations and forecasts of the marine environmental conditions in the Aegean Sea. The buoy network collects meteorological, sea state and upper-ocean physical and biochemical data. The efficiency and functionality of the various system components are being evaluated during the present pre-operational phase and discussed in this paper. The problem of bio-fouling on optical and chemical sensors is found to be a main limitation factor on the quality of data. Possible solutions to this problem as well as quality control methods that are being developed are also described. Finally, an evaluation of the numerical models is presented through the estimation of their forecasting skill for selected periods.

Animals↗
PubMed2004

Fine forecasts: encouraging the media to include ultraviolet radiation information in summertime weather forecasts.

Melanoma and skin cancer are largely attributable to over-exposure to solar ultraviolet radiation (UVR). Reports of UVR levels within media weather forecasts appear to be well received by the public and have good potential to communicate the need for appropriate sun protection to a broad audience. This study describes provision of UVR messages by New Zealand newspapers, radio and TV stations over a 4-year period, and examines the impact of an intervention which aimed to address media concerns about the financial costs of accessing UVR information for broadcast. Newspaper and radio presentation of UVR information increased immediately after these costs were removed; however, among radio stations it dropped in subsequent years. Among those media that were presenting UVR, this information was broadcast throughout the summer period and repeated throughout the day. Furthermore, most reports included concomitant, behavioral sun protection messages. At the final assessment (summer 2001/02), 66% of radio stations, both TV channels and 48% of newspapers reported UVR information in summertime weather reports. Efforts to further increase UVR presentation will need to address media concerns about time constraints on weather forecasts and media perceptions of poor audience demand or understanding of some UVR messages.

Female↗
PubMed2005

[Application of phenological pattern recognition in ecological dynamic forecasting].

This paper described the principles, methods, and procedures of ecological dynamic forecasting by the automation techniques of pattern recognition and mathematical logic judgment on the basis of phenological data and model output maps from T42L9 numerical weather prediction model. This new forecasting method proposed on the basis of modern meteorology and automation techniques enables the classic phenology to apply to a new field ecological forecasting. It enables phenological forecasting to develop from single-station forecasting stage to regional forecasting stage, which is greatly corresponded to the development stage from single station forecasting stage to synoptic stage in weather forecasting, and enables agro-meteorological forecasting to develop from qualitative and statistical forecasting stage to ecological dynamic forecasting stage. With this new qualitative forecasting method, both the predicted objective and predictors are of considerable bio-physical interests. The ecological dynamic forecasting method could be applied to crop sowing, crop growth, irrigation and fertilization, and diseases and pests

Computer Simulation↗
PubMed2003

Pollen count forecasting.

Pollen forecasting is becoming increasingly important to allergists as an adjunct to effective patient care. Forecasts allow patients to avoid exposure to high pollen levels and prompt them to take prophylactic medication and to plan outdoor activities for periods of low pollen levels. Investigators are making progress in developing effective models for daily and seasonal forecasts for important pollen allergens; however, current models are limited to specific geographic areas. Models for the onset of the season for spring tree pollen are based on the chilling and heat units that are required before flowering can occur. Models for pollen season severity are based on regression analysis of preseason meteorologic conditions, and models for daily forecasts are based on the normal pollen curve and responses to day-to-day meteorologic conditions. When winds are favorable, long-distance transport can introduce allergenic pollen types into a local area. The Mountain Cedar Pollen Forecasting model, which combines day-to-day release forecasts at source areas and dispersion forecasts to downwind areas, has been reasonablly successful over the past 4 years. All pollen forecasting models are dependent on accurate meteorologic forecasts, and pollen forecasting will become more accurate as meteorologic forecasts improve.

Forecasting↗
PubMed1992

Use of forecasting signatures to help distinguish periodicity, randomness, and chaos in ripples and other spatial patterns.

Forecasting of one-dimensional time series previously has been used to help distinguish periodicity, chaos, and noise. This paper presents two-dimensional generalizations for making such distinctions for spatial patterns. The techniques are evaluated using synthetic spatial patterns and then are applied to a natural example: ripples formed in sand by blowing wind. Tests with the synthetic patterns demonstrate that the forecasting techniques can be applied to two-dimensional spatial patterns, with the same utility and limitations as when applied to one-dimensional time series. One limitation is that some combinations of periodicity and randomness exhibit forecasting signatures that mimic those of chaos. For example, sine waves distorted with correlated phase noise have forecasting errors that increase with forecasting distance, errors that are minimized using nonlinear models at moderate embedding dimensions, and forecasting properties that differ significantly between the original and surrogates. Ripples formed in sand by flowing air or water typically vary in geometry from one to another, even when formed in a flow that is uniform on a large scale; each ripple modifies the local flow or sand-transport field, thereby influencing the geometry of the next ripple downcurrent. Spatial forecasting was used to evaluate the hypothesis that such a deterministic process-rather than randomness or quasiperiodicity-is responsible for the variation between successive ripples. This hypothesis is supported by a forecasting error that increases with forecasting distance, a greater accuracy of nonlinear relative to linear models, and significant differences between forecasts made with the original ripples and those made with surrogate patterns. Forecasting signatures cannot be used to distinguish ripple geometry from sine waves with correlated phase noise, but this kind of structure can be ruled out by two geometric properties of the ripples: Successive ripples are highly correlated in wavelength, and ripple crests display dislocations such as branchings and mergers.

Journal Article↗
PubMed1990

The relationship between the length of the base period and population forecast errors.

"The base period of a population forecast is the time period from which historical data are collected for the purpose of forecasting future population values. The length of the base period is one of the fundamental decisions made in preparing population forecasts, yet very few studies have investigated the effects of this decision on population forecast errors. In this article the relationship between the length of the base period and population forecast errors is analyzed, using three simple forecasting techniques and data from 1900 to 1980 for states in the United States. It is found that increasing the length of the base period up to 10 years improves forecast accuracy, but that further increases generally have little additional effect. The only exception to this finding is long-range forecasts of rapidly growing states, in which a longer base period substantially improves forecast accuracy for two of the forecasting techniques."

Americas↗
PubMed2005

Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): an alternate for nonstructural flood management in Bangladesh.

Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

Bangladesh↗
PubMed1986

Determination of an optimal forecast model for ambulance demand using goal programming.

A multistep approach to determining the optimal parameters of an exponential smoothing model was used to forecast emergency medical service (E.M.S.) demand for four counties of South Carolina. Daily emergency and routine (non-emergency) demand data were obtained and forecast statistics generated for each county sampled, using Winters' exponential smoothing model. A goal programme was formulated to combine forecast results for emergency calls with routine call forecasts. The goal programme gave a higher priority to accurate forecasting of emergency demand. The forecast model generated implicitly weights demand by severity and provides a reliable estimate of demand overall. The optimal parameter values for the smoothing model were obtained by minimizing the objective function value of the goal programming problem. The parameter values obtained were used to forecast demand for E.M.S. in the selected counties. The results of the model were compared to those using a multiple linear regression model and a single-objective-based exponential smoothing model for 2 months of data. When compared with two single-objective forecast models, the multiple-objective approach yielded more accurate forecasts and, therefore, was more cost-effective for the planner. The model presents and demonstrates a theoretical approach to improving the accuracy of ambulance demand forecasts. The possible impact of this approach on planning efficiency is discussed.

Ambulances↗