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PubMed2002

Activities of the Japanese space weather forecast center at Communications Research Laboratory.

The International Space Environment Service (ISES) is an international organization for space weather forecasts and belongs to the International Union of Radio Science (URSI). There are eleven ISES forecast centers in the world, and Communications Research Laboratory (CRL) runs the Japanese one. We make forecasts on the space environment and deliver them over the phones and through the Internet. Our forecasts could be useful for human activities in space. Currently solar activity is near maximum phase of the solar cycle 23. We report the several large disturbances of space environment occurred in 2001, during which low-latitude auroras were observed several times in Japan.

Extraterrestrial Environment↗

PubMed1990

[Daily Japanese cedar pollen forecast in Sendai].

The forecast of daily Japanese cedar (Cryptomeria japonica) pollen counts was performed in Sendai in 1987, 1988 and 1989. The expected daily maximum temperature was used as a main determinative factor, and the term "pollen index", which is a ratio of a daily pollen count to the rest of the total seasonal pollen count, was devised and proved to be closely related to the daily maximum temperature. In 1987 and 1989, the total seasonal pollen count was very low, and daily pollen counts were in the lowest of three grades, except for a few days of the middle grade. In 1988, a fairly large number of pollen grains were observed, and forecasts of low grade were made for 28 days, middle grade for 25 days and high grade for 10 days, respectively. On the other hand, the actual daily pollen counts of low grade were 36 days, middle grade, 15 days, and high grade, 12 days, respectively. The accuracy of the forecast was 67% in 1988. The reasons for errors and the assignments for the forecast were discussed.

Forecasting↗

PubMed1994

No excuses: forecasting can be managed.

Originally written for manufacturers, the forecasting concepts presented in this article are easily applied to a hospital or health care facility with a supply function or central distribution center. Forecasting can offer a method of managing the control of medical supplies, pharmaceuticals, or maintenance items. A statistical based forecasting approach with seasonality testing could be useful in analyzing patient census data, calculating trends, and then recommending staffing levels. Cost containment and efficiency of operations are priorities in any industry. Forecasting the demand of medical supplies and services in today's hectic health care environment can have a significant effect on both customer service and financial results. The bottom line is happy patients and healthy profits.

Connecticut↗

PubMed1987

Forecasting hospital reimbursement.

The changes in reimbursement to hospitals by third party payers make it essential for hospital administrators to forecast their hospitals' reimbursement potential. Although models have been developed to predict and maximize census, forecast future demand for hospital care, and forecast the revenue budget, none of the models addresses how the physician directly affects the hospital reimbursement potential through office practice admissions. This study analyzes the admitting physician's patients by third party payer and presents a method to assist in using a physician's past hospital admissions to forecast a hospital's admissions and reimbursement potential.

Data Collection↗

PubMed2001

Four methodologies to improve healthcare demand forecasting.

Forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. This task, which often is assumed by financial managers, first requires the compilation and examination of historical information. Although many quantitative forecasting methods exist, four common methods of forecasting are percent adjustment, 12-month moving average, trendline, and seasonalized forecast. These four methods are all based upon the organization's recent historical demand. Healthcare financial managers who want to project demand for healthcare services in their facility should understand the advantages and disadvantages of each method and then select the method that will best meet the organization's needs.

Decision Making, Organizational↗

PubMed1997

[Population forecasts 1996: less population growth, increased aging].

"According to the new national population forecasts for the Netherlands for the period 1996-2050, the population size will continue to grow for another three decades. It will reach a maximum of 17.2 million, lower than the maximum according to the previous population forecasts.... The reason for this lower growth is that the assumption with respect to the future level of fertility is revised downwards.... Another adjustment of the forecasts is the assumption that life expectancy at birth will continue to increase until 2050, whereas in the previous forecasts life expectancy was held constant after 2010.... As a consequence of these revised assumptions the percentage of elderly people will increase more strongly." (SUMMARY IN ENG)

Age Distribution↗

PubMed1976

Developing a demand forecasting system for a foodservice operation.

In foodservice operations, accurate and dependable forecasts of food production demands can help control food and labor costs. A decreased incidence of menu item over- and under-production should lower scheduled labor and production time and optimize use of equipment. Each foodservice system has specified characteristics and patterns of activity. A procedure to develop, establish, control, and evaluate a forecasting system is described. The objectives of the foodservice and the proposed forecasting system must be defined. A cycle menu and historical data bases are two key inputs. It is more accurate to forecast menu item demand than diet category demand because of the complexity in categorizing multi-restricted diets. Control of the system is maintained by establishing policies and procedures and conducting routine subjective and objective evaluations.

Computers↗

PubMed2002

[Ecological forecasting: a frontier in ecology].

An evolving science of ecological forecasting is beginning to emerge, and could have an increasingly important role in policy-making and management of natural resources and environment. The progress in computer science, quantitative analysis and ecological theory, together with the application of new high technology, will increase our ability to forecast ecosystem change. The authors introduced the connotation of ecological forecasting, relevant research advances, and some typical examples. Ecological forecasting is an important frontier in ecology, and also, would be an important direction for future ecological study.

Decision Making↗

PubMed1989

Forecasting mortality: a parameterized time series approach.

This article links parameterized model mortality schedules with time series methods to develop forecasts of U.S. mortality to the year 2000. The use of model mortality schedules permits a relatively concise representation of the history of mortality by age and sex from 1900 to 1985, and the use of modern time series methods to extend this history forward to the end of this century allows for a flexible modeling of trend and the accommodation of changes in long-run mortality patterns. This pilot study demonstrates that the proposed procedure produces medium-range forecasts of mortality that meet the standard tests of accuracy in forecast evaluation and that are sensible when evaluated against the comparable forecasts produced by the Social Security Administration.

Adolescent↗

PubMed1988

Stability over time in the distribution of population forecast errors.

A number of studies in recent years have investigated empirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of time periods during the 20th century, comparing these projections with census enumerations to determine forecast errors, and analyzing the stability of the resulting error distributions over time. These data are then used to construct and test empirical confidence limits. We find that in this sample the distribution of absolute percentage errors remained relatively stable over time and data on past forecast errors provided very useful predictions of future forecast errors.

Demography↗

PubMed1988

Forecasting menu-item demand in foodservice operations.

The state of the practice in forecasting menu-item demand in foodservice operations was determined. A survey to assess the forecasting techniques utilized by foodservice directors was administered to a random sample of 834 American Dietetic Association Members with Management Responsibilities in Health Care Delivery System. Statistical analysis of the 392 responses revealed that less than 25% of the practitioners were using mathematical models in forecasting menu-item demand. Naive models were utilized by the majority of the respondents. The moving average technique was the most frequently used (14.8%) mathematical model. Approximately 75% of the practitioners indicated that continuing education is needed relative to the forecasting techniques that may be employed in foodservice management.

Food Preferences↗

PubMed1986

Population forecasts and confidence intervals for Sweden: a comparison of model-based and empirical approaches.

This paper compares several methods of generating confidence intervals for forecasts of population size. Two rest on a demographic model for age-structured populations with stochastic fluctuations in vital rates. Two rest on empirical analyses of past forecasts of population sizes of Sweden at five-year intervals from 1780 to 1980 inclusive. Confidence intervals produced by the different methods vary substantially. The relative sizes differ in the various historical periods. The narrowest intervals offer a lower bound on uncertainty about the future. Procedures for estimating a range of confidence intervals are tentatively recommended. A major lesson is that finitely many observations of the past and incomplete theoretical understanding of the present and future can justify at best a range of confidence intervals for population projections. Uncertainty attaches not only to the point forecasts of future population, but also to the estimates of those forecasts' uncertainty.

Forecasting↗

PubMed1979

A time series approach to forecasting Australian total live-births.

The relationship between classical demographic deterministic forecasting models, stochastic structural econometric models and time series models is discussed. Final equation autoregressive moving average (ARMA) models for Australian total live-births are constructed. Particular attention is given to the problem of transforming the time series to stationarity (and Gaussianity) and the properties of the forecasts are analyzed. Final form transfer function models linking births to females in the reproductive age groups are also constructed and a comparison of actual forecast performance using the various models is made. Long-run future forecasts are generated and compared with available projections based on the deterministic cohort model after which some policy implications of the analysis are considered.

Australia↗

PubMed1982

Analysis of meal census patterns for forecasting menu item demand.

The purpose of this research was to identify functional relationships between the patient census and the official institutional census recorded at midnight for hospital inpatients and for mental health patients cared for in a satellite facility at the University of Missouri-Columbia Health Sciences Center. Investigation of these relationships was necessary to provide knowledge essential for the design of a statistical menu item forecasting system. Patient census for three meals (breakfast, lunch, and supper) was compared with the midnight census, using graphical analysis and analysis of variance. Reliable patterns were identified in the patient census data which required accommodation in a mathematical forecasting model. Three forecasting design options were identified and appraised. The simplest design option and the two more complex design options forecasted identical quantities.

Dietetics↗

PubMed1997

Assessing Risk in Operational Decisions Using Great Lakes Probabilistic Water Level Forecasts

/ A method adapted from the National Weather Service's Extended Streamflow Prediction technique is applied retrospectively to three Great Lakes case studies to show how risk assessment using probabilistic monthly water level forecasts could have contributed to the decision-mak-ing process. The first case study examines the 1985 International Joint Commission (IJC) decision to store water in Lake Superior to reduce high levels on the downstream lakes. Probabilistic forecasts are generated for Lake Superior and Lakes Michigan-Huron and used with riparian inundation value functions to assess the relative impacts of the IJC's decision on riparian interests for both lakes. The second case study evaluates the risk of flooding at Milwaukee, Wisconsin, and the need to implement flood-control projects if Lake Michigan levels were to continue to rise above the October 1986 record. The third case study quantifies the risks of impaired municipal water works operation during the 1964-1965 period of extreme low water levels on Lakes Huron, St. Clair, Erie, and Ontario. Further refinements and other potential applications of the probabilistic forecast technique are discussed.KEY WORDS: Great Lakes; Water levels; Forecasting; Risk; Decision making

Journal Article↗

PubMed2003

Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens.

A comparison study has been performed with neural networks (NNs) and multiple linear regression models to forecast the next day's maximum hourly ozone concentration in the Athens basin at four representative monitoring stations that show very different behavior. All models use 11 predictors (eight meteorological and three persistence variables) and are developed and validated between April and October from 1992 to 1999. Performance results based on a wide set of forecast quality measures indicate that the NNs provide better estimates of ozone concentrations at the monitoring sites, whilst the more often used linear models are less efficient at accurately forecasting high ozone concentrations. The violation of the European information threshold of 180 microg/m(3) is successfully predicted by the NN in 72% of the cases on average. Results at all stations are consistent with similar ozone forecast studies using NNs in other European cities.

Journal Article↗

PubMed1994

Time series forecasts of emergency department patient volume, length of stay, and acuity.

STUDY HYPOTHESIS: Time series analysis can provide accurate predictions of emergency department volume, length of stay, and acuity. DESIGN: Prospective stochastic time series modeling. SETTING: A university teaching hospital. INTERVENTIONS: All patients seen during two sequential years had time of arrival, discharge, and acuity recorded in a computer database. Time series variables were formed for patients arriving per hour, length of stay, and acuity. Prediction models were developed from the year 1 data and included five types: raw observations, moving averages, mean values with moving averages, seasonal indicators with moving averages, and autoregressive integrated moving averages. Forecasts from each model were compared with observations from the first 25 weeks of year 2. Model accuracy was tested on residuals by autocorrelation functions, periodograms, linear regression, and confidence intervals of the variance. RESULTS: There were 42,428 patients seen in year 1 and 44,926 in year 2. Large periodic variations in patient volume with time of day were found (P < .00001). The models based on arithmetic means or seasonal indices with a single moving average term gave the most accurate forecasts and explained up to 42% of the variation present in the year 2 test series. No time series model explained more that 1% of the variation in length of stay or acuity. CONCLUSION: Time series analysis can provide powerful, accurate short-range forecasts of future ED volume. Simpler models performed best in this study. Time series forecasts of length of stay and patient acuity are not likely to contribute additional useful information for staffing and resource allocation decisions.

Emergency Service, Hospital↗

PubMed2006

Malaria early warnings based on seasonal climate forecasts from multi-model ensembles.

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean-atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

Animals↗