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PubMed2006

Forecasting from ignorance: the use and usefulness of recognition in lay predictions of sports events.

Whereas previous studies on how people make forecasts of sports events focused primarily on experts, we examined how laypeople do this task. In particular, we (a) tested the recognition heuristic [Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological Review, 109, 75-90], which requires partial ignorance, against four alternative mechanisms in describing laypeople's forecasts for the European Soccer Championships 2004; (b) evaluated how well recognition predicted the outcomes of the matches compared to direct indicators of team strength (e.g., past performance, rankings); and (c) studied the less-is-more effect--the phenomenon that knowing less leads to more correct forecasts than knowing more--which can occur when the recognition heuristic is used. Two groups of participants (laypeople, experts) made forecasts for the first-round matches of the tournament. Of the five candidate mechanisms, the recognition heuristic predicted laypeople's forecasts best: when applicable, it accounted for 90% of the forecasts. The recognition heuristic correctly predicted the actual winner of the matches substantially better than chance but did not achieve the accuracy of direct indicators of team strength. The experts made more correct forecasts than the laypeople. Moreover, we found no benefit of ignorance among the group of laypeople, although the conditions for a less-is-more effect specified by Goldstein and Gigerenzer were fulfilled.

Adolescent↗

PubMed2005

Forecasting the prognosis of choroidal melanoma with an artificial neural network.

PURPOSE: To develop an artificial neural network (ANN) that will forecast the 5-year mortality from choroidal melanoma. DESIGN: Retrospective, comparative, observational cohort study. PARTICIPANTS: One hundred fifty-three eyes of 153 consecutive patients with choroidal melanoma (age, 58.4+/-14.6 years) who were treated with ruthenium 106 brachytherapy between 1988 and 1998 at the Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel. METHODS: Patients were observed clinically and ultrasonographically (A- and B-mode standardized ultrasonography). Metastatic screening included liver function tests and liver imaging. Backpropagation ANNs composed of 3 or 4 layers of neurons with various types of transfer functions and training protocols were assessed for their ability to predict the 5-year mortality. The ANNs were trained on 77 randomly selected patients and tested on a different set of 76 patients. Artificial neural networks were compared based on their sensitivity, specificity, forecasting accuracy, area under the receiver operating curves, and likelihood ratios (LRs). The best ANN was compared with the results of logistic regression and the performance of an ocular oncologist. MAIN OUTCOME: The ability of the ANNs to forecast the 5-year mortality from choroidal melanoma. RESULTS: Thirty-one patients died during the follow-up period of metastatic choroidal melanoma. The best ANN (one hidden layer of 16 neurons) had 84% forecasting accuracy and an LR of 31.5. The number of hidden neurons significantly influenced the ANNs' performance (P<0.001). The performance of the ANNs was not significantly influenced by the training protocol, the number of hidden layers, or the type of transfer function. In comparison, logistic regression reached 86% forecasting accuracy, with a very low LR (0.8), whereas the human expert forecasting ability was <70% (LR, 1.85). CONCLUSIONS: Artificial neural networks can be used for forecasting the prognosis of choroidal melanoma and may support decision-making in treating this malignancy.

Brachytherapy↗

PubMed2005

The New England Air Quality Forecasting Pilot Program: development of an evaluation protocol and performance benchmark.

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as prototypes, to forecast ozone (O3) concentrations across the northeastern United States during the summer of 2002. A suite of statistical metrics was identified as part of the protocol that facilitated evaluation of both discrete forecasts (observed versus modeled concentrations) and categorical forecasts (observed versus modeled exceedances/nonexceedances) for both the maximum 1-hr (125 ppb) and 8-hr (85 ppb) forecasts produced by each of the models. Implementation of the evaluation protocol took place during a 25-day period (August 5-29), utilizing hourly O3 concentration data obtained from over 450 monitors from the U.S. Environment Protection Agency's Air Quality System network.

Air Pollution↗

PubMed1997

Value-added forecasting.

Long ago, the emphasis shifted away from forecasting as a competitive weapon when it became apparent that forecast error could never be eliminated. Forecasts became a necessary evil that no one wanted to claim responsibility for. It's time to clear up some of the misconceptions about forecasts and to seize the opportunity inherent in the forecasting process. It is not forecast accuracy but rather improved understanding and use of forecasting as a tool for reducing both costs and lead times that will add real value to an enterprise and can improve the results from any and all other initiatives.

Commerce↗

PubMedDate not supplied

Continuous urea monitoring in hemodialysis: a model approach to forecast dialytic performance. Results of a multicenter study.

BACKGROUND: A urea biosensor, inserted into the ultrafiltrate collection-line of paired filtration dialysis (PFD), not only allows on-line dialysis quantification, but also forecasts final (Cend) and 30 min equilibrated urea concentration (Ceq), the most reliable value for calculating dialysis efficiency. The urea biosensor processes plasma ultrafiltrate continuously, delivering a large amount of data to the computer, which estimates the parameters by a mathematical model, thus predicting the whole urea profile with rebound. METHODS: A multicenter randomized trial on 41 patients was conducted to ascertain the ability of a two-pool variable-volume urea model to forecast Cend and Ceq at 60 and 90 min after the start of dialysis. Two alternative dialytic treatments, A or B, were chosen, the latter being more efficient. Each treatment included six serial PFD. The accuracy of forecasting was evaluated through four indices based on forecast errors, calculated as the difference between observed and forecasted urea values: mean percent error (MPE) (%), mean absolute deviation (MAD) (mg/dl), mean absolute percent error (MAPE) (%) and root mean squared error (RMSE) (mg/dl). RESULTS: Forecasted urea concentrations were lower than those measured by the biosensor. MPE for Cend was negligible in A (+1.2%) and much higher in B (+7.2%); both values improved at 90 min, +1.0% and +5.8%, respectively. MAD for Cend was similar in both treatments and improved slightly at 90 min, ranging from 4.9 to 5.9 mg/dl. MPE for Ceq was +4% in A and and more than doubled in B (+11.5%); both values improved at 90 min, +3.7% and +9.7%, respectively. MAD for Ceq was 7.5 mg/dl in A and 8.5 mg/dl in B; both improved at 90 min, 6.7 and 7.4 m g/dl, respectively. The other indices, MAPE and RMSE, showed similar results. Comparison between the errors of the two treatments with analysis of variance (ANOVA) for repeated measures gave no significant results. CONCLUSIONS: Our model forecasts of urea concentrations were overall lower than the measured ones: the bias was negligible for A-Cend, greater for the A-Ceq and when the more efficient treatment B was used. The 60 min predictions improved at 90 min. The comparison between the prediction errors in the two treatments were not statistically significant. The recirculation measurement would probably reduce the bias if it were properly incorporated into the model.

Filtration↗

PubMed2005

INFERNO: a system for early outbreak detection and signature forecasting.

OBJECTIVE: Public health surveillance systems that monitor daily disease incidence provide valuable information about threats to public health and enable public health authorities to detect enteric outbreaks rapidly. This report describes the INtegrated Forecasts and EaRly eNteric Outbreak (INFERNO) detection system of algorithms for outbreak detection and forecasting. METHODS: INFERNO incorporates existing knowledge of infectious disease epidemiology into adaptive forecasts and uses the concept of an outbreak signature as a composite of disease epidemic curves. RESULTS: Four main components comprise the system: 1) training, 2) warning and flagging, 3) signature forecasting, and 4) evaluation. The unifying goal of the system is to gain insight into the nature of temporal variations in the incidence of infection. Daily collected records are smoothed initially by using a loess-type smoother. Upon receipt of new data, the smoothing is updated; estimates are made of the first two derivatives of the smoothed curve, which are used for near-term forecasting. Recent data and near-term forecasts are used to compute a five level, color-coded warning index to quantify the level of concern. Warning algorithms are designed to balance false detection of an epidemic (Type I errors) with failure to correctly detect an epidemic (Type II errors). If the warning index signals a sufficiently high probability of an epidemic, the fitting of a gamma-based signature curve to the actual data produces a forecast of the possible size of the outbreak. CONCLUSION: Although the system is under development, its potential has been demonstrated through successful use of emergency department records associated with a substantial waterborne outbreak of cryptosporidiosis that occurred in Milwaukee, Wisconsin, in 1993. Prospects for further development, including adjustment for seasonality and reporting delays, are also outlined.

Algorithms↗

PubMed1988

Forecasting the demand on accident and emergency departments in health districts in the Trent region.

The annual new, return and total attendances at Accident and Emergency (A and E) Departments for Trent district and the whole of the Trent region are forecast for the years 1986 to 1994 by using the autoregressive integrated moving average (ARIMA) time series model applied to the SH3 A and E returns for 1974 to 1985. The 1986 forecasts of annual new, return and total attendances in Trent districts are compared with the actual attendances observed; the new attendance forecasts were found accurate, the return attendance forecasts less so. The latter may reflect inability to predict changing policies on return attendances of individual A and E departments. The 1994 ARIMA forecasts of annual A and E new attendances for Trent districts are compared with the 1984 based regional guidelines for 1994 and the projections for individual districts. Both the ARIMA models and the health districts' own projections produce a different forecast to the 1994 regional guideline which seems to overestimate. The forecasting methodology used has other applications in health care planning.

Accidents↗

PubMed2004

A new method of ozone forecasting using fuzzy expert and neural network systems.

This study describes the method of forecasting daily maximum ozone concentrations at four monitoring sites in Seoul, Korea. The forecasting tools developed are fuzzy expert and neural network systems. The hourly data for air pollutants and meteorological variables, obtained both at the surface and at the high elevation (500 hPa) stations of Seoul City for the period of 1989-1999, were analyzed. Two types of forecast models are developed. The first model, Part I, uses a fuzzy expert system and forecasts the possibility of high ozone levels (equal to or above 80 ppb) occurring on the next day. The second model, Part II, uses a neural network system to forecast the daily maximum concentration of ozone on the following day. The forecasting system includes a correction function so that the existing model can be updated whenever a new ozone episode appears. The accuracy of the forecasting system has been improved continuously through verification and augmentation.

Journal Article↗

PubMed2000

Do forecasts of UV indexes influence people's outdoor behaviour?

OBJECTIVE: To investigate Australian adults' awareness of the ultraviolet (UV) indexes forecast in the media, and whether these UV forecasts influence their behaviour in the sun. METHODS: A self-administered questionnaire was used on two occasions in 1997 to ask about knowledge of UV indexes shown in the media and about possible influence on outdoor behaviour. SETTING AND PARTICIPANTS: Participants were 977 residents (423 men; 554 women) of Nambour originally randomly selected in 1986 from the electoral roll, who have been followed up subsequently. RESULTS: The majority of people--92% of men and 86% of women--reported having seen or heard the UV indexes forecast during summer. Of these, significantly fewer men (107; 28%) than women (209; 46%) reported that their outdoor behaviour was influenced by knowledge of the forecast (p = 0.001). Neither age nor skin type, nor history of sunburns or skin cancer, affected knowledge of UV forecasts or their influence on behaviour. CONCLUSIONS AND IMPLICATIONS: Although most people are aware of the forecasts of UV indexes in the media, the majority do not take them into account in their outdoor behaviour. Compared with women, men were more aware of, but less influenced by, forecasts of UV indexes. Better communication of the implications of the UV indexes is needed, particularly to men, if they are to adapt their outdoor behaviour to improve their sun protection.

Adult↗

PubMed2004

Performance of time-series methods in forecasting the demand for red blood cell transfusion.

BACKGROUND: Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. STUDY DESIGN AND METHODS: Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. RESULTS: The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. CONCLUSION: Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

Erythrocyte Transfusion↗

PubMed1992

Stochastic population forecasts and their uses.

"The properties and uses of stochastic forecasts are discussed here. For linear stochastic projections, we show how the computation of forecast moments and the statistical distribution of forecasts depend on the multiplicative and autoregressive structure of the dynamics. Both scalar and vector projection methods are discussed, and their similarities are explored. Next we discuss the uses of stochastic forecasts, arguing that it is important to relate forecasts to the specific decision-making criteria of particular forecast users. The example of [the U.S. system of] Social Security is used to show how a dynamic programming approach may be used to explore alternative decisions in a probabilistic context."

Americas↗

PubMed1997

Scenarios, uncertainty and conditional forecasts of the world population.

This study is concerned with the methods available for the forecasting of future trends in the world's population. Particular attention is given to the problem of the uncertainties that these forecasts include. "The purpose of this paper is to show how subjective and data-based probabilistic assessments of error can be combined, to give a user a realistic assessment of the uncertainty of demographic forecasts, and to apply these concepts to forecasts of the world population. Moreover, we shall show how conditional forecasts can provide a simple conceptual framework in which to view scenarios. They can be particularly useful in the evaluation of proposed policies. Indeed, the so-called environmental impact assessments...that are now mandatory in many countries for major construction projects typically contain elements of conditional forecasting." The concepts discussed are illustrated by comparing a scenario of future global population growth prepared at the Institute of Applied Systems Analysis with a UN population projection.

Forecasting↗

PubMed1996

[Population and household forecasts 1995].

"The Netherlands Population and Household Forecasts are updated annually by Statistics Netherlands. In the 1995-based forecasts the short-term assumptions on fertility and immigration are revised downwards in comparison with the previous forecasts. As a result the forecast of total population size is lower: according to the new forecasts total population size in 2010 will be 16.7 million compared with 16.8 million according to the previous forecast." (SUMMARY IN ENG)

Demography↗

PubMed1995

[Evaluation of Dutch population forecasts].

The author assesses the accuracy of population forecasts in the Netherlands. "Forecast errors may have been caused by either failures of the forecast model or by developments which do not correspond to the expectations. Due to large errors of forecasts made in the 1960s and 1970s a new forecasting model (the cohort-component-model) has been developed and used since. Important changes of trends in fertility, mortality and external migration were incorporated gradually in the hypotheses of successive forecasts. However, short term fluctuations may sometimes lead to incorrect adjustments of long term assumptions." (SUMMARY IN ENG)

Demography↗

PubMed1992

Forecasting hospital laboratory procedures.

Improved forecasts of hospital laboratory procedures can provide the basis for better resource planning and enhanced operating efficiency. The research reported here-in describes how multiple regression models can be both a source of insight into causal relationships and a tool for achieving accurate monthly forecasts. Past research in this area may have overstated the statistical significance of findings because of a failure to address the potential effect of serial correlation. The present study uses the Cochrane-Orcutt regression procedure, rather than OLS, to overcome this problem. A model using inpatient admissions, acuity days, length of stay, discharge days and seasonal dummy variables is shown to account for 87% of the variation in the number of billable laboratory procedures. A simpler multiple regression model and a Winters' exponential smoothing model were found to provide excellent forecasts for laboratory procedures. In a one year out of sample evaluation, the annual percent forecast error was 0.7% for the regression model. This compares favorably to a percentage forecast error of 11.6% using subjective forecasting methods.

Forecasting↗

PubMed1979

Statistical forecasting in a hospital clinical laboratory.

Three forecasting methodologies were applied to monthly laboratory test count data in order to arrive at a best procedure for forecasting ahead to cover the next fiscal year. The purpose of the forecasting was, first, to aid in reimbursement and income decisions and, second, to assist in operations management decisions within the laboratory itself. The Box-Jenkins ARIMA models were found to be superior in all cases, and forecasts for individual test counts (as opposed to packages of tests billed as a unit) were improved if forecasts for inpatients and outpatients were done separately and then aggregated. With 2 years of experience to go on, the annual forecast error stands at around 4.5%.

Clinical Laboratory Techniques↗

PubMed1989

Forecasting systems in managing hospital services demand: a review of applicability and a measure of utility.

A comprehensive literature review of forecasting methodologies and their specific applications to managing hospital services demand provided a credible base for the ensuing study of current forecasting usage. A sample of 40 hospitals was analyzed to measure the current perceived urgency to utilize forecasting systems. These findings were then compared with perceived actual usage. The incidence of formal forecasting systems actually being utilized was lower than the perceived need to use such systems. Identification of principal methodologies utilized and an assessment of computer-assisted forecasting indicated that a strong reliance on qualitative, manually-derived methodologies still remains. Correlation analyses of key exogenous variables indicated that the larger sized hospitals utilized computerized methodologies and had the highest measures of perceived need for, and actual practice of, formal forecasting programs.

Evaluation Studies as Topic↗

PubMed2004

Using forecasting techniques to predict meal demand in Title IIIc congregate lunch programs.

The purpose of this study was to determine which forecasting model would most accurately predict meal demand in Title IIIc congregate lunch programs designed for serving older adults. Forecasting techniques including naïve, moving average (three versions) and simple exponential smoothing were applied to data collected over a 4-month period from seven meal sites located in a large urban area. An analysis of the forecasting models using mean absolute deviations and mean squared errors indicated that simple mathematical forecasting techniques provided better predictions of meal demand than did the naïve method for all sites. In four of the seven sites, exponential smoothing was the best forecasting model, whereas in the remaining sites, moving average models provided the best forecast. Implications are discussed.

Aged↗