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PubMed1986

Birth forecasting based on birth order probabilities, with application to U.S. data.

"A model for birth forecasting based on prediction of the so-called 'birth order probabilities' is constructed. The relation between this model and recent models of fertility prediction is derived. Birth forecasts with approximate probability limits for the U.S. for the period 1983-1997 are generated. The performance of the proposed model in predicting future fertility is tested by fitting time series models to part of the available series (1917-1982) and ultimately generating birth forecasts for the remainder of the period, then comparing these forecasts with the actual data." The accuracy of the fertility forecasts made are compared with those made by other methods.

Americas↗

PubMed1986

Methods for national population forecasts: a review.

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."

Cohort Studies↗

PubMed1990

Error models for official mortality forecasts.

"The Office of the Actuary, U.S. Social Security Administration, produces alternative forecasts of mortality to reflect uncertainty about the future.... In this article we identify the components and assumptions of the official forecasts and approximate them by stochastic parametric models. We estimate parameters of the models from past data, derive statistical intervals for the forecasts, and compare them with the official high-low intervals. We use the models to evaluate the forecasts rather than to develop different predictions of the future. Analysis of data from 1972 to 1985 shows that the official intervals for mortality forecasts for males or females aged 45-70 have approximately a 95% chance of including the true mortality rate in any year. For other ages the chances are much less than 95%."

Americas↗

PubMed1983

A method for disaggregate household forecasts.

A method for making household forecasts is proposed "which yields both the total number of households and the number present in any predetermined subgroup of households. The method presupposes that incomplete information is available on the transitions of persons between various age and household categories over a period of time. The forecasting method is capable of spelling out the consequences for the prognoses of adding different amounts of external information to the computations. Such information may include population forecasts by age and sex, trend-based information like restrictions on the size distribution of households, econometric relations between household formation and incomes, etc." The forecasting method is described in theoretical terms. "This description includes the use of information-theoretic arguments to adjust the forecast to external data. The theoretical development is illustrated by an application of the method to data for the Stockholm region [of Sweden]. In relation to this application an outline is given of the use of the methodology for long-term projections."

Demography↗

PubMed1981

Forecasting Australian marriage rates.

"The paper discusses the problem of modelling demographic variables for the purpose of forecasting. Two empirical model selection procedures are applied to suggest final form forecasting equations for Australian marriage rates. The suggested models are then assessed by comparing their post-sample forecast performance with that of univariate ARMA-type models of marriage rates which are regarded as approximations to marriage rate final equation models. In this instance the ARMA models are preferred for forecasting purposes. The properties of the ARMA model forecasts are then examined and the modelling strategy is contrasted with the regression method used by Withers."

Australia↗

PubMed1990

[Accuracy of forecasts of annual numbers based on monthly numbers].

"Since 1985 monthly forecasts have been made of yearly numbers of births, deaths, immigration, emigration and marriages [in the Netherlands] on the basis of monthly figures. The forecasts are equal to the arithmetic average of an ARIMA projection and a naive forecast.... This article compares the forecasts for the years 1985-1989 with forecasts obtained from a simple alternative method." (SUMMARY IN ENG)

Developed Countries↗

PubMed1995

[Population and household forecasts, 1994].

"The Netherlands Population and Household Forecasts are updated annually by Statistics Netherlands.... In the 1994 based forecasts the median age at marriage is expected to be one year higher than in the previous forecasts (from 29 to 30 years for men born in 1985 and 27 to 28 years for women born in 1985). Also, the median age at first childbirth is expected to be a half year higher than in previous forecasts. Furthermore, non-married cohabitation is expected to rise even faster than in former forecasts.... The increase in one-person households during this period will be about 610 thousand and the number of couples (with or without children) will increase by 330 thousand." (SUMMARY IN ENG)

Age Factors↗

PubMed1992

The use of time-series analysis to forecast bont tick (Amblyomma hebraeum) infestations in Zimbabwe.

Studying the dynamics of tick infestations on cattle is an essential step in developing optimal strategies for tick control. Successful strategic tick control requires accurate predictions of when tick infestations will reach predetermined threshold levels. In the case of Amblyomma hebraeum, earlier work has shown that there is no consistent pattern of seasonal activity. This means that a statistical model for predicting A. hebraeum infestations cannot reliably use climatic factors as the only independent variables. An alternative method is to apply time-series, or auto-regressive moving-average (ARMA), analysis which uses only the past population patterns to predict future trends. This technique was applied to a data set consisting of 108 weekly tick counts of A. hebraeum (adult males, standard females, flat females and standard nymphs), conducted at an experimental station in southeastern Zimbabwe. The ability of the ARMA models to fit and predict actual tick infestations was judged using two sets of criteria. The first set focused on the goodness-of-fit, and used the adjusted R2 values, Q statistic and the Akaike Information Criteria. The second set of criteria measured the forecasting accuracy of an estimated equation, and consisted of regressing a 9-period forecast against an actual out-of-sample data set not used in the estimation process. The root mean square error of the forecast was also considered when comparing several models for the same data set. Using these criteria, the models estimated using the ARMA technique were judged to both fit and forecast with sufficient accuracy to warrant their use in strategic tick control. Although the success of using ARMA to forecast A. hebraeum is partly due to the non-seasonal behavior of the species, the results presented here suggest that it is worthwhile exploring the use of ARMA techniques to model the dynamics of other tick species. Where independent variables exert considerable influence on the dynamics of a tick species, these variables can be incorporated into an ARMA-style model.

Animals↗

PubMed2006

Estimating air quality in a traffic tunnel using a forecasting combination model.

This study compared three forecasting methods based on their accuracy or absolute errors in forecasting air pollution in a traffic tunnel: the Grey model (GM), the Crank-Nicholson implicit scheme model, and the forecasting combination model (FCM). Three criteria, root mean square error (RMSE), the mean absolute error (MAE) and mean absolute percentage error (MAPE), were applied to the models and the FCM model displayed all of the characteristics of a good forecasting model. The correlation coefficient (r) for the FCM model equaled 0.94 (Upwind), 0.98 (Middle) and 0.98 (Downwind). This study indicated that FCM can be used to accurately forecast CO pollution in the Kaohsiung Cross Harbor Tunnel.

Air Pollutants↗

PubMed1991

Forecasting road accident casualties in Great Britain.

There is considerable interest in many countries in the way in which their annual road accident casualty totals have developed. This leads inevitably to the question of how these totals are likely to change in the future. This paper assesses national data for Great Britain from 1949-1989 and forecasts the casualty total in the year 2000 by extrapolating the long-term decline in the rate of casualties per hundred million vehicle-kilometres. This forecast is conditional on the traffic growth predicted by that year, but it is found that uncertainty over the casualty forecast derives more from the slight irregularities in the past decline in the casualty rate than from uncertainty over the traffic prediction. Despite the use of a forecasting method that is based on an unusually strong time-series model, there is still real uncertainty about forecasting casualties over a gap of only 11 years.

Accidents, Traffic↗

PubMed2001

Performance of stochastic approaches for forecasting river water quality.

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

Environmental Monitoring↗

PubMed2001

Forecasting models for human resources in health care.

This article is a review of the approaches published between 1996 and 1999 that have been used to forecast human resource requirements for nursing. Much of the work to date generally does not consider the complex factors that influence health human resources (HHR). They also do not consider the effect of HHR decisions on population health, provider outcomes such as stress, and the cost of a decision made. Supply and demand approaches have dominated. Forecasting is limited, too, by the availability of reliable and valid data bases for examining supply and use of nursing personnel across sectors. Three models--needs based, utilization based, and effective demand based--provide substantially different estimates of future HHR need. The methods of analysis employed for forecasting range from descriptive to predictive and are borrowed from demography, epidemiology, economics, and industrial engineering. Simulation models offer the most promise for the future. The forecasting methods described have demonstrated their accuracy and usefulness for specific situations, but none has proven accurate for long-term forecasting or for estimating needs for large geographical areas or populations.

Forecasting↗

PubMed1990

Effects of targets and aggregation on the propagation of error in mortality forecasts.

"Official forecasts of mortality depend on assumptions about target values for the future rates of decline in mortality rates. Smooth functions connect the jump-off (base-year) mortality to the level implied by the targets. Three alternative sets of targets are assumed, leading to high, middle, and low forecasts. We show that this process can be closely modeled using simple linear statistical models. These explicit models allow us to analyze the error structure of the forecasts. We show that the current assumption of perfect correlation between errors in different ages, at different forecast years, and for different causes of death, is erroneous. An alternative correlation structure is suggested, and we show how its parameters can be estimated from the past data. The effect of the level of aggregation on the accuracy of mortality forecasts is considered." The geographical focus is on the United States. (SUMMARY IN FRE)

Age Factors↗

PubMed2004

Carbon monoxide concentration forecasting in Santiago, Chile.

In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.

Air Pollutants↗

PubMed2006

A Bayesian forecasting model: predicting U.S. male mortality.

This article presents a Bayesian approach to forecast mortality rates. This approach formalizes the Lee-Carter method as a statistical model accounting for all sources of variability. Markov chain Monte Carlo methods are used to fit the model and to sample from the posterior predictive distribution. This paper also shows how multiple imputations can be readily incorporated into the model to handle missing data and presents some possible extensions to the model. The methodology is applied to U.S. male mortality data. Mortality rate forecasts are formed for the period 1990-1999 based on data from 1959-1989. These forecasts are compared to the actual observed values. Results from the forecasts show the Bayesian prediction intervals to be appropriately wider than those obtained from the Lee-Carter method, correctly incorporating all known sources of variability. An extension to the model is also presented and the resulting forecast variability appears better suited to the observed data.

Bayes Theorem↗

PubMed2006

Comparison of forecasting methodologies using egg price as a test case.

Egg price forecasting of shelled eggs is a complex problem. Traditionally, future egg price has been predicted using a combination of regression analysis and experienced-based intuition to build a model, which is then fine-tuned to prevalent market conditions. Even after collecting reliable and expensive data, the subsequent analysis, in many cases, does not produce a high confidence to explain the variations in egg price. In the current project, a different approach using neural networks was used to forecast egg price. A neural network is a mathematical model of an information-processing structure that is loosely based on our present understanding of the working of human brain. An artificial neural network consists of a large number of simple processing elements connected to each other in a network. Urner Barry egg quotes from 1991 to 2002 as well as number of hens, egg storage capacity, and number of eggs placed for hatching from the USDA databases (1993 to 2000) were used to forecast egg price. Regression analysis explained only 37% of the variation in egg price due to the above-mentioned 3 factors. Neural networks, on the other hand, recognize the pattern in previous years' egg prices and then predict the future price more efficiently. The 3 networks used in this research (Ward, back-propagation, and general regression neural networks) fit the forecast line more tightly to the previous year's egg price line than did regression analysis. In the case of general regression neural networks, the R2 value was as high as 60%. Results suggest that neural networks may be a more reliable method of egg price forecasting than simple regression analysis if reliable data are collected and manipulated for such models.

Agriculture↗

PubMed1979

Forecasting bed needs and recommending facilities plans for community hospitals: a review of past performance.

A university-based hospital consulting group reviewed six studies of Michigan hospitals retrospectively in 1975. The studies represented all those done between 1967 and 1971 requiring forecasts of acute bed supply and service needs. The original studies developed forecasts using empirical studies of patient origin and rigorously prepared authoritative forecasts of county populations. The 1975 review compared forecasts of population, service population, and bed need against current values and also interviewed clients to assess retrospective satisfaction with the recommendations. Although the consultants strove steadily to minimize the bed supply and base population forecasts were accurate, the studies overestimated bed needs. Further, the clients were often dissatisfied with the original recommendations, and frequently acted to exceed them. Comparing the 1975 actual with what would now be recommended by the consultant indicates that the "error" cost the communities about $50 per person per year.

Bed Occupancy↗

PubMed2005

Operational seasonal forecasting of crop performance.

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

Agriculture↗