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PubMed2005

[Artificial neural network forecasting method in monitoring technique by spectrometric oil analysis].

The spectrometric oil analysis (SOA) is an important technique for machine state monitoring and fault diagnosis, and forecasting machine state through SOA results has an advantage of finding out machine system wear fault early. Because Artificial Neural Network (ANN) possesses obvious advantages over traditional forecasting models for identifyingnon-linear model and forecasting non-even signal, the ANN forecasting approach was applied to monitoring technique by SOA, and the monitoringtechnique by SOA based on ANN forecasting was put forward. In the forecasting model, a 3-layer BP network structure was adopted.Aiming at the problem that ANN structure has a great effect on forecasting precision, the authors utilized the Genetic Algorithm (GA) to optimize the node number of input layer, the node number of hidden layer, and MSE (Mean of Squared Error) target value which was required for ANN training, and obtained the optimum forecasting model of ANN. Finally, the practical SOA data of some engine was analyzed and forecasted by ANN, and the forecasting result was compared with that of traditional ARMA model. The result fully showsthe superiority and effectivity of the new method.

Algorithms↗
PubMed2005

Constructing a 7-day ahead forecast model for grass pollen at north London, United Kingdom.

BACKGROUND: A number of media outlets now issue medium-range ( approximately 7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. OBJECTIVE: The objective of this study is to construct a medium-range (</=7 day) forecast model for grass pollen at north London. METHOD: The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990 to 1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post-peak periods of grass pollen release. The forecast consisted of five regression models: two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. RESULTS: Overall, the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. CONCLUSION: This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Air Pollutants↗
PubMed1994

Exponential smoothing method for forecasting drug expenditures.

A model for forecasting a hospital pharmacy drug budget is described, and its results in 10 hospital pharmacy departments are evaluated. A model for forecasting inpatient drug expenditures was developed based on the method of exponential smoothing. Exponential smoothing predicts a value based on the forecast for the prior period, with adjustment for the error of that forecast. Recent data are weighted more heavily than older data; as data become older, weights decline exponentially. The model incorporates changes in workload in addition to drug expenditure data. The variable used for workload can vary from one hospital to another, depending on the statistics that are available. The model was designed to be more accurate than current methods, easy and quick to use, and usable with a minimal knowledge of statistics and forecasting theory. The model was tested on fiscal 1988-1992 drug budget data from 10 British Columbia hospital pharmacy departments. Four departments had insufficient data; of the remaining six, the forecasting model performed better than the hospitals' current methods in four departments. The mean absolute deviation between budgeted (by current methods) and actual drug expenditures was 8.70% (range, 6.19-15.16%). The forecasting model yielded a mean absolute deviation of 5.93% (range, 3.13-7.66%). Better forecasts resulted when pharmacy medication-order volume was used as a workload variable, as compared with hospital inpatient days. An exponential smoothing model improved the accuracy of drug-budget forecasts in four of six pharmacy departments.

British Columbia↗
PubMed2005

Effects of seasonal climate forecasts and participatory workshops among subsistence farmers in Zimbabwe.

Improvements in the ability to model El Niño and other large-scale interannual climate variations have allowed for the development of seasonal climate forecasts, predicting rainfall and temperature anomalies for many places around the world. These forecasts have allowed developing countries to predict shortfalls in grain yields, with benefits for food security. Several countries communicate the forecasts to subsistence farmers, which could allow them to mitigate the effects of drought on their harvests by adapting their cropping decisions accordingly. However, it has not been demonstrated that subsistence farmers benefit from having access to the forecasts. Here we present evidence of subsistence farmers using the forecasts over multiple years to make different decisions and significantly improving their harvests when they do so. In a controlled study, farmers in Zimbabwe who reported adapting their farming methods to seasonal climate forecasts significantly improved their harvests over baseline amounts. Moreover, farmers who had attended a brief workshop and learned more about the forecasts were significantly more likely to use the forecasts than were farmers who learned of the forecasts through nonparticipatory channels.

Journal Article↗
PubMed2005

Feeling at risk matters: water managers and the decision to use forecasts.

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.

Journal Article↗
PubMed1977

A model for statistical forecasting of menu item demand.

Foodservice planning necessarily begins with a forecast of demand. Menu item demand forecasts are needed to make food item production decisions, work force and facility acquisition plans, and resource allocation and scheduling decisions. As these forecasts become more accurate, the tasks of adjusting original plans are minimized. Forecasting menu item demand need no longer be the tedious and inaccurate chore which is so prevalent in hospital food management systems today. In most instances, data may be easily collected as a by-product of existing activities to support accurate statistical time series predictions. Forecasts of meal tray count, based on a rather sophisticated model, multiplied by average menu item preference percentages can provide accurate predictions of demand. Once the forecasting models for tray count have been developed, simple worksheets can be prepared to facilitate manual generation of the forecasts on a continuing basis. These forecasts can then be recorded on a worksheet that reflects average patient preference percentages (of tray count), so that the product of the percentages with the tray count prediction produces menu item predictions on the same worksheet. As the patient preference percentages stabilize, data collection can be reduced to the daily recording of tray count and one-step-ahead forecase errors for each meal with a periodic gathering of patient preference percentages to update and/or verify the existing date. The author is more thoroughly investigating the cost/benefit relationship of such a system through the analysis of new empirical data. It is clear that the system offers potential for reducing costs at the diet category or total tray count levels. It is felt that these benefits transfer down to the meal item level as well as offer ways of generating more accurate predictions, with perhaps only minor (if any) labor time increments. Research in progress will delineate expected savings more explicitly. The approach requires statistical and computer expertise primarily during the development of the tray count model and patient preference percentage table. The results of this effort can be transferred to a form that is easily utilized by food management personnel manually to generate menu item demand forecasts.

Computers↗
PubMed1993

Forecast intervals of net migration: the case of the Netherlands.

This study addresses problems concerning the forecasting of net migration in the preparation of population forecasts. "As the width of forecast intervals for migration in single years differs strongly from that of an interval for average migration during the forecast period, it is important that the forecaster indicates which type of interval is presented. A comparison of forecast intervals for net migration obtained from an ARIMA model to intervals in official Dutch national population forecasts shows that the uncertainty on migration has been underestimated in past official forecasts."

Demography↗
PubMed1997

Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region.

Forecasting models for first, return and total attendances at accident and emergency (A&E) departments and yearly forecasts were developed ten years ago for all the health districts in the Trent region in England. The one-yearly forecasts had been checked against the 1986 actual figures and found accurate for first attendances but less accurate for return attendances. The forecasts for 1993 and 1994 were much further from the actual figures than the 1986 forecasts, with an increasing bias towards overestimation, particularly for reattendances. Whether a first attender is reviewed at a further visit may depend on local medical policy, which itself may vary with personnel changes. The one-off original ARIMA forecasts for new attendances for 1994 were no better than the district projections made in 1984, but they were better than the Trent Regional Health Authority guidelines. The ten-year strategic plan for Trent Regional Health Authority overestimated the increase in the number of first attendances at A&E departments in the Trent region. The forecasting methodology on which it was based could be improved by incorporating the ARIMA method into planning at the health district level. New forecasts or updated ones need to be calculated yearly.

Accidents↗
PubMed2006

A 30-day-ahead forecast model for grass pollen in north London, United Kingdom.

A 30-day-ahead forecast method has been developed for grass pollen in north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21 May to 8 August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961 to 1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of 1 to 4; the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of 1 to 4, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002, respectively, when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

Climate↗
PubMed2004

Forecasting clinical disease in pigs: comparing a naive and a Bayesian approach.

In veterinary practice the clinician often evaluates and predicts herd health status over time according to clinical criteria. In this paper, we modeled three different clinical signs among pigs based on longitudinal clinical observations in 15 pig herds. We compared and discussed the outputs from two different approaches for making clinical forecasts in a herd: a naive approach using a simple time series model with previous disease observations as predictors and a Bayesian state space models approach, in which the time lag variable entered into the random component of the model. We used the Markov chain Monte Carlo technique to calculate posterior distributions of the forecasts. For the herd specific forecasts the results showed that there were only minor differences between the forecasts from the simple time series model and the median forecasts from the Bayesian model. However, the credibility intervals from the Bayesian model were wider than the forecasts from the simple model and, therefore the Bayesian model encompassed the variability in the forecasts better. Compared to the statistical model, the simple time series would be easier to implement in a practical setting. However, the latter lacks the inherent "generality" from the statistical model that allows the user to make statements about the distribution of the herds and to predict disease status based on the "average" correlation among the herds. The applicability of the Bayesian approach within a clinical decision-making framework was discussed, with special emphasis on the use of prior information and clinical forecasting.

Animals↗
PubMed2004

Seasonal forecast of St. Louis encephalitis virus transmission, Florida.

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empiric relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill-verification analyses may be applied to test the predictability of an empiric disease forecast model.

Animals↗
PubMed1991

Forecasting in foodservice: model development, testing, and evaluation.

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

Food Preferences↗
PubMed1990

Survey of foodservice production forecasting.

Assessment of the state of practice in forecasting production demand in foodservice operations is needed to provide a base for research in model development and implementation. A survey to document the forecasting techniques utilized by foodservice directors was administered to the 464 members of the National Association of College and University Food Services; 282 questionnaires (60.7%) were returned. Fewer than 16% of the respondents used mathematical models for forecasting demand. The moving average technique was the most frequently used mathematical model (8.5%). Variations of the naive or nonmathematical model were used by the majority of respondents. Judgment based on the past records was the most frequently cited variation (89.4%). The foodservice director/manager was the person most frequently responsible for forecasting decisions (83.7%). Typically, determining production demand was conducted 1 week in advance (36.5%). Computers were used by fewer than 38% of respondents for production forecasting. Approximately 79% indicated that forecasting was very important. Respondents indicated a need for improvement in practice and additional training in the area of forecasting. Therefore, continuing education in forecasting should remain a priority for dietetic practitioners.

Chi-Square Distribution↗
PubMed1981

Issues in forecasting graduate dental education manpower supply and requirements.

This paper explores the history of forecasting in dentistry and then focuses on several major forecasting techniques, briefly examining the basic assumptions, data requirements, and strengths and weaknesses of each. In discussing the issues involved in forecasting, the paper isolates three perspectives held by forecasters; health status, social need, and economic. Each approach defines the critical concept of "adequate supply" in a different way. Other issues addressed include: who should engage in forecasting; what methods should be employed; is it worth the effort to forecast requirements for dental specialists; should forecasting be done for small areas; and what is the "political" reality of forecasting.

Dental Care↗
PubMed1979

Forecasting individual pharmacokinetics.

Often drug dosage may be chosen rationally by use of plasma concentration (CP) as the "therapeutic" end point. The ability to accurately forecast the CP resulting from a dosage regimen is central to choosing that regimen. Tradionally forecasting has been attempted only by accounting for known influences on pharmacokinetics, such as sex, age, and renal disease. One must also adjust for previously observed CPs. Herein, we discuss and explain an approach to both of these tasks, mainly focusing on the latter. The approach balances observed outcomes against prior expectations taking account of observation CP error. For digoxin, use of 1 measured CP, as opposed to none, improves forecast precision for future CPs by 40% (decrement in variance of forecast error), and 2 CPs improve it by 67%. There is also an increase in forecast accuracy (decrement in mean of forecast error) as the number of CPs used increases. After only 2, forecast accuracy and precision are as good as theoretically possible. Moreover, information from CPs is far more valuable for forecasting than that from observable patient features-sex, age, and the like; use of all the latter information does not improve accuracy and precision as much as only 1 CP.

Adult↗
PubMed1995

Disaggregation in population forecasting: do we need it? And how to do it simply.

"We have described a method for reducing the dimensionality of the forecasting problem by parsimoniously modeling the evolution over time of the age schedules of vital rates. This method steers a middle course between forecasting aggregates and forecasting individual age specific rates: we reduce the problem to forecasting a single parameter for fertility and another one for mortality. We have described a number of refinements and extensions of those basic methods, which preserve their underlying structure and simplicity. In particular, we show how one can fit the model more simply, incorporate lower bounds to the forecasts of rates, disaggregate by sex or race, and prepare integrated forecasts of rates for a collection of regions. We also discuss alternate approaches to forecasting the estimated indices of fertility and mortality, including state-space methods. These many versions of the basic method have yielded remarkably similar results." (SUMMARY IN FRE)

Age Distribution↗
PubMed2007

Forecasting age-specific breast cancer mortality using functional data models.

Accurate estimates of future age-specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age-specific breast cancer mortality time trends, and forecast entire age-specific mortality functions using a state-space approach. We use annual unadjusted breast cancer mortality rates in Australia, from 1921 to 2001 in 5 year age groups (45 to 85+). We use functional data analysis techniques where mortality and incidence are modelled as curves with age as a functional covariate varying by time. Data are smoothed using non-parametric smoothing methods then decomposed (using principal components analysis) to estimate basis functions that represent the functional curve. Period effects from the fitted coefficients are forecast then multiplied by the basis functions, resulting in a forecast mortality curve with prediction intervals. To forecast, we adopt a state-space approach and an automatic modelling framework for selecting among exponential smoothing methods.Overall, breast cancer mortality rates in Australia remained relatively stable from 1960 to the late 1990s, but have declined over the last few years. A set of four basis functions minimized the mean integrated squared forecasting error and account for 99.3 per cent of variation around the mean mortality curve. Twenty year forecasts suggest a continuing decline, but at a slower rate, and stabilizing beyond 2010. Forecasts show a decline in all age groups with the greatest decline in older women. The proposed methods have the potential to incorporate important covariates such as hormone replacement therapy and interventions to represent mammographic screening. This would be particularly useful for evaluating the impact of screening on mortality and incidence from breast cancer.

Aged↗
PubMed2003

An assessment of predictive forecasting of Juniperus ashei pollen movement in the Southern Great Plains, USA.

Juniperus ashei pollen, a significant aeroallergen, has been recorded during December and January in Tulsa, Oklahoma, over the past 20 years. The nearest upwind source for this pollen is populations growing in southern Oklahoma and central Texas, at distances of 200 km and 600 km respectively. Long-distance dispersal of J. ashei pollen into the Tulsa area shows a strong correlation with the trajectories of wind blowing across southern populations before traveling north towards eastern Oklahoma. The strong tie between climatic conditions and the occurrence of this aeroallergen within the Tulsa, Oklahoma, atmosphere provided a unique opportunity to forecast the dispersal, entrainment, and downwind deposition of this significant aeroallergen. Forecasts of long-distance J. ashei pollen dispersal began during the winter of 1998/1999. Each forecast uses defined climatic parameters to signal pollination at each source site. Coupled to these estimates of pollen release, forecast weather conditions and modeled wind trajectories are used to determine the threat of dispersal to downwind communities. The accuracy of these forecasts was determined by comparing the forecast "threat" to aerobiological records for the same period collected in the "Tulsa region". Analysis of the two seasons revealed only a single occurrence of "high" or "very high" pollen concentrations in Tulsa not directly linked to "moderate" or "severe" forecast threats from the southern source areas.

Forecasting↗