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PubMed2006

Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.

Algorithms↗

PubMed2005

The use of injury surveillance databases to identify emerging injury hazards.

Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R(2) = 0.86, F(3, 27) = 55.2, p < 0.0001). Significant contributions to the model included the SD (beta = 1.60, p < 0.001), mean monthly frequency (beta = -0.72, p < 0.002), and the seasonality of the data (beta = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.

Accident Prevention↗

PubMed2003

Improving risk adjustment for Medicare capitated reimbursement using nonlinear models.

OBJECTIVES: This article compares a linear risk-adjusted model of medical expenditures for Medicare patients with a model that explicitly account for skewness in distribution of expenditures. METHODS: A model of expenditures and a model of the square root of expenditures, each expressed as linear combinations of risk adjusters, are estimated using data from the 1992 through 1994 Medicare Current Beneficiary Surveys. Five sets of risk adjusters are considered. Each combination of model and set of risk adjusters is tested for linearity, heteroscedasticity, in-sample fit (R2), forecast performance (forecast bias and forecast mean squared error), and overfitting the data. We analyze forecast performance (1)based on forecasts in same year used for estimation, and (2)based on forecasts in the year following that used for estimation. RESULTS: In the first analysis, the model using a square root transformation of expenditures as the dependent variable and the more parsimonious specification of risk adjusters performs best in terms of forecast squared error and overfitting. The untransformed model performs best in terms of forecast bias in each group based on severity of disability, with the exception of the severely disabled for whom the square root model is best. In the second analysis, the square root model performs better than the untransformed model in terms of forecast squared error, but neither model is statistically distinguishable from zero in terms of bias. CONCLUSIONS: Accounting for skewness in expenditures tends to improve precision but not necessarily bias, except among the severely disabled. Adjusting for health status improves risk adjustment.

Activities of Daily Living↗

PubMed2005

Urban meteorological modelling for nuclear emergency preparedness.

The main objectives of the current EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure" (FUMAPEX) are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction (NWP) models to urban air pollution and population dose models, the building of improved urban air quality information and forecasting systems, and their application in cities in various European climates. In addition to the forecast of the worst air-pollution episodes in large cities, the potential use of improved weather forecasts for nuclear emergency management in urban areas, in case of hazardous releases from nuclear accidents or terror acts, is considered. Such use of NWP data is tested for the Copenhagen metropolitan area and the Øresund region. The Danish Meteorological Institute (DMI) is running an experimental version of the HIRLAM NWP model over Zealand including the Copenhagen metropolitan area with a horizontal resolution of 1.4km, thus approaching the city-scale. This involves 1-km resolution physiographic data with implications for the urban surface parameters, e.g. surface fluxes, roughness length and albedo. For the city of Copenhagen, the enhanced high-resolution NWP forecasting will be provided to demonstrate the improved dispersion forecasting capabilities of the Danish nuclear emergency preparedness decision-support system, the Accident Reporting and Guidance Operational System (ARGOS), used by the Danish Emergency Management Agency (DEMA). Recently, ARGOS has been extended with a capability of real-time calculation of regional-scale atmospheric dispersion of radioactive material from accidental releases. This is effectuated through on-line interfacing with the Danish Emergency Response Model of the Atmosphere (DERMA), which is run at DMI. For local-scale modelling of atmospheric dispersion, ARGOS utilises the Local-Scale Model Chain (LSMC), which makes use of high-resolution DMI-HIRLAM NWP model data provided to DEMA by DMI four times a day under operational surveillance and covering Denmark and surroundings. The integration of DERMA in ARGOS is effectuated through automated on-line digital communication and exchange of data. The calculations are carried out in parallel for each NWP model to which DMI has access, thereby providing a mini-ensemble of dispersion forecasts for the emergency management.

Air Pollutants, Radioactive↗

PubMed2004

Measure of predictability.

Many techniques have been developed to measure the difficulty of forecasting data from an observed time series. This paper introduces a measure which we call the "forecast entropy" designed to measure the predictability of a time series. We use attractors reconstructed from the time series and the distributions in the regular and tangent spaces of the data which comprise the attractor. We then consider these distributions on different scales. We present a formula for calculating the forecast entropy. To provide a standard of predictability, we define an idealized random system whose forecast entropy will be maximal; we then use this measure to rescale the forecast entropy to lie in the range [0,1]. The time series obtained from several chaotic systems as well as from a pseudorandom system are studied using this measure. We present evidence that the forecast entropy can be used as a tool for determining optimal delays and embedding dimensions used for reconstructing better attractors. We also show that the forecast entropy of a random system has completely different characteristics from that of a deterministic one.

Biophysics↗

PubMed2003

[Study on meteorological factors-based neural network model of malaria].

OBJECTIVE: In order to provide reliable data for strategies development on prevention, a meteorological factors-based predicating model for malaria forecast was studied. METHODS: Data on malaria occurrence and climate changes from 1994 to 1999 in counties in Yunnan province was collected and analyzed with software packages of FoxPro 6.0 and Excel 5.0. The forecasting model for malaria occurrence was established, using the Neural Network Toolbox of Matlab 6.1 software package. In the studies of forecasting model, data of malaria and meteorological factors from 1994 to 1999 in Honghe state in Yunnan province was chosen. The meteorological factors included average monthly pressure, air temperature, relative humidity, monthly maximum air temperature, minimum air temperature, rainfall, rainday, evaporation and sunshine hours in the study. The established forecasting model was also tested and verified. RESULTS: The BP network model was established according to data of diseases and meteorological factors from Honghe state in Yunnan province. After training the neural network for 100 times, the error of performance decreased from 3.23608 to 0.035862. Verified by fact data of malaria, the efficiency of malaria forecasting was 84.85%. CONCLUSION: Neural network model was effective for forecasting malaria. It showed advantages as: strong ability for analysis, lower claim for data, convenient and easy to apply etc. Neural network model might be used as a new method for malaria forecasting.

Humans↗

PubMed2000

A universal pattern of mortality decline in the G7 countries.

Human lifespan has increased enormously this century. But we remain uncertain about the forces that reduce mortality, and about the cost implications of ageing populations and their associated social burden. The poor understanding of the factors driving mortality decline, and the difficulty of forecasting mortality are due in part to the pronounced irregularity of annual to decadal mortality change. Here we examine mortality over five decades in the G7 countries (Canada, France, Germany, Italy, Japan, UK, US). In every country over this period, mortality at each age has declined exponentially at a roughly constant rate. This trend places a constraint on any theory of society-driven mortality decline, and provides a basis for stochastic mortality forecasting. We find that median forecasts of life expectancy are substantially larger than in existing official forecasts. In terms of the costs of ageing, we forecast values of the dependency ratio (that is, the ratio of people over 65 to working people) in 2050 that are between 6% (UK) and 40% (Japan) higher than official forecasts.

Aging↗

PubMedDate not supplied

How accurate are the U.S. Bureau of the Census projections of total live births.

"The accuracy of total live birth forecasts issued by the U.S. Bureau of the Census was analysed. Forecast accuracy has not improved significantly since 1950. Further, the forecasts are not more accurate than several naive alternatives. Moving from a period methodology to a cohort methodology improved forecast accuracy for certain forecasts. [It is demonstrated that] the Bureau of the Census systematically underestimated total births in the upswing and overestimated in the downswing."

Americas↗

PubMed1997

An age- and sex-structured HIV epidemiological model: features and applications.

An important challenge in modelling the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemic is to use the increasing quantity of disease surveillance data to validate estimates and forecasts. Presented is a novel model for forecasting HIV incidence by age and sex and among sentinel groups for which data are available. This approach permits a closer relationship between forecasting and surveillance activities, and more accurate estimates validated to data. As inputs the model uses an estimate of the HIV prevalence, country demographic data, and a profile of the sexual risk of HIV infection by age, to project HIV incidence, prevalence, number of AIDS cases and population. The following examples of the use of the model are given: forecasting HIV incidence in East Africa, by age, sex, and among pregnant women; 3-5-year forecasts of HIV incidence; modelling mixed risk behaviour HIV epidemics in South-east Asia; demographic indicators; and targeting a preventive vaccine by age group.

Adolescent↗

PubMed1998

Tools for immunization guideline knowledge maintenance. II. Automated Web-based generation of user-customized test cases.

IMM/Test is a prototype software tool built to generate test cases that can be used to help test and verify the internal logic of an immunization forecasting program. A forecasting program takes as input a child's immunization history and produces recommendations as to which vaccinations are due and which should be scheduled next. IMM/Test was developed to test a specific immunization forecasting program, IMM/Serve. In addition, IMM/Test has been incorporated into a broader Web-based tool, IMM/Web, which allows the user (e.g., a member of an immunization registry staff) to customize the parameters used for immunization forecasting (e.g., the minimum ages for each dose and the minimum wait intervals between doses) to reflect local practice. IMM/Web then generates a customized set of test cases that may be used to test the user's immunization forecasting program. The user may also request that the test cases be automatically passed to IMM/Serve to analyze using the newly defined parameters. The paper describes the internal design of IMM/Test and IMM/Web and discusses certain lessons learned in the implementation of the two programs.

Adolescent↗

PubMed2006

Prisoners' positive illusions of their post-release success.

Prisoners' forecasts of post-release success may have implications for how they respond to imprisonment, release, and parole decisions. We examined sentenced US and UK prisoners' forecasts of recidivism, and how well UK prisoners believed they would fare compared to the average other prisoner. In both samples, forecasts of recidivism were unrealistically optimistic when compared to official statistics on recidivism. UK prisoners also demonstrated a self-enhancement bias by forecasting that they were less likely to re-offend than other prisoners. Prisoners' forecasts of recidivism were predicted by only a few of the pre-prison, in-prison, and post-prison factors that have been shown to be associated with actual recidivism. We discuss the implications of these findings and propose avenues for future research.

Adaptation, Psychological↗

PubMed1995

Managing the medical workforce: time for improvements?

There has been a regular cycle of Government committees in Britain that have forecast 'shortages' and 'surpluses' in medical manpower. It is remarkable how little change there has been in the methods used in these forecasts despite criticism of their methods and the move towards competitive markets in the delivery of health care. The practice of medical manpower planning was criticised in the 1960s because of the failure to explore the implications of alternative skill mixes and incentives. Whilst these criticisms were ignored, the forecasters now pay more regard to budget constraints and produce more regular estimates. The 1992 forecast could be improved by sensitivity analysis (e.g. for GP/population ratios). However, unless the issue of scope (forecasting for all health care staff, not just for doctors alone) and the implications of skill mix and incentives identified in the 1960s are addressed effectively, the current recommendations may produce doctor 'surpluses' in the early twenty-first century. The Government's analysis should be challenged at the level of principle and practice, and this implies that the research activity proposed in the 1960s is carried out in the 1990s to facilitate efficiency in the twenty-first century. Furthermore, the issue of policy priorities and their ranking needs to be spelt out explicitly: does the Government want a competitive NHS internal market, and if it does, what is the role of central manpower planning? One implication of current policy choice and the maintenance of manpower planning is that cost containment is of more importance to policy makers than the internal market.

Forecasting↗

PubMed2003

Prediction of the spread of influenza epidemics by the method of analogues.

This study was designed to examine the performance of a nonparametric forecasting method first developed in meteorology, the "method of analogues," in predicting influenza activity. This method uses vectors selected from historical influenza time series that match current activity. The authors applied it to forecasting the incidences of influenza-like illnesses (ILI) in France and in the country's 21 administrative regions, using a series of data for 938 consecutive weeks of ILI surveillance between 1984 and 2002, and compared the results with those for autoregressive models. For 1- to 10-week-ahead predictions, the correlation coefficients between the observed and forecasted regional incidences ranged from 0.81 to 0.66 for the method of analogues and from 0.73 to -0.09 for the autoregressive models (p < 0.001). Similar results were obtained for national incidence forecasts. From the results of this method, maps of influenza epidemic forecasts can be made in countries in which national and regional data are available.

Bias↗

PubMedDate not supplied

Patients with right frontal lesions are unable to assess and use advice to make predictive judgments.

Frontal lobe damage impairs decision-making. Most studies have employed gambling and probabilistic tasks, which have an emotional (reward-punishment) component and found that patients with ventromedial sector lesions have exceptional difficulty performing normally on these tasks. We have recently presented an economic decision-making task to patients and normal volunteers that required them to not only forecast an economic outcome but also to weigh advice from four advisors about the possible outcome across 40 trials. We studied 20 patients with right frontal lobe lesions and 9 patients with parietal lobe lesions and compared their performance to 20 matched controls. Frontal lobe lesion patients were inconsistent at using advice and their forecasts were poor. Patients with dorsolateral but not orbito-frontal lesions showed some ability to assess advice. Patients with parietal lobe lesions were good at assessing advice but were slow at doing so; they were consistent but poor at using advice and their use of advice was unrelated to their forecasting. All three patient groups were overconfident in their own performance. In contrast, controls could both use and assess advice, their ability to use advice was mediated by their ability to assess it, and they were not overconfident. Group differences on an overall measure of accuracy on this task were associated with an ability to accurately plan. Differences in ability to assess and forecast were associated with planning and working memory performance. These findings indicate that patients with both right dorsolateral and orbito-frontal lesions may be impaired when required to make complex decisions related to forecasting and judgment. Our findings enlarge the scope of decision-making deficits seen in patients with frontal lobe lesions and indicate additional circumstances in which patients with frontal lobe lesions will have difficulty in deciding.

Adult↗

PubMed2003

Study of the relationship between the onset of peptic ulcers and meteorological factors.

OBJECTIVE: To discuss the relationship between the onset of peptic ulcers (PU) and meteorological factors (MF). METHODS: In reviewing records from 17 hospitals in the city of Nanning from 1992 to 1997, we found 24, 252 cases of PU in 104, 121 samples of gastroscopic examinations. We then calculated the detectable rate of PU (DRPU) during each season every five days (FD) and made a correlated analysis with the seasonal MF during the same period in Nanning. Finally, we made a multiple regressive correlated analysis of DRPU and the 5MF for the same period of the year. A forecast model based on the MF of the previous FD was established. The real value and the forecast value was being tested and verified. RESULTS: From 1992 to 1997, the DRPU is: winter and spring > summer and autumn (P < 0.005). There is a close relationship between the DRPU and the average temperature (AT), the average highest temperature (AHT), the average lowest temperature (ALT), average air pressure (AAP) and the average dew point temperature (ADT) of the five days of the same period of the year (the correlated coefficients are -0.5348, -05167, -0.5384, 0.4579 and -0.4936, respectively), with P < 0.01. The AT, AHT, ALT, AAP and ADT of the previous FD are of great value in forecasting the onset of PU, with its real value and forecast value corresponding to 66.6%. CONCLUSIONS: There exists a close relationship between DRPU and the AT, AHT, ALT, AAP and ADT of the FD for the same period. A mid-term medical meteorological forecast of the onset of PU can be made more accurately and reliably according to the close relation between the DRPU and some MF of the previous FD.

Forecasting↗

PubMed1993

Why estimates of physician supply and requirements disagree.

OBJECTIVE: To review the major forecasts of physician supply and physician requirements. DATA SOURCES: English-language medical literature on physician manpower published between 1980 and 1990, identified from journal articles selected through searches of the MEDLINE and Health Planning and Administration databases using the search formulations physician supply, health manpower--physicians, and physician manpower. STUDY SELECTION: Sources were selected if they were repeatedly cited and provided quantitative projections. DATA EXTRACTION: Each study was reviewed for its quantitative projections and to identify its methodological assumptions. DATA SYNTHESIS: All forecasts point to a steadily increasing physician supply and, with one exception, suggest that supply will exceed requirements in the year 2000. The estimates of physician supply across studies varied primarily because of differing assumptions about entrance rates into the profession and the size and clinical work load of specific categories of physicians: researchers, teachers, administrators, residents, and women. The estimates of physician requirements were more volatile because of differences in the basic approach and assumptions about future growth and market structure. CONCLUSIONS: Even though the measurement of entrance and exit rates from the profession is a generally accepted approach to forecasting supply, apparently minor disagreements on assumptions create large discrepancies between forecasts over time. There is no accepted approach to forecasting physician requirements.

Forecasting↗

PubMedDate not supplied

A comparative study of autoregressive neural network hybrids.

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modeling the residuals. In this paper, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents' performances. We demonstrate this using nine data sets, autoregressive linear and time-delay neural network models.

Algorithms↗

PubMed2000

Time-dependent transition probabilities and the assessment of seasonal effects on within-day variations in chewing behaviour of housed sheep.

State transitions in the chewing behaviour of six half-breed (Île de FrancexTexel) yearling female sheep (Ovis aries L.) were studied by using jaw movements recorded continuously over 5 days at the end of a number of experimental periods from 21 September 1992 to 4 April 1993. The sheep were housed in individual pens. Each of them received the same diet, that is, 250 g/day of concentrate mix (15.5% crude protein (CP), 36.5% neutral detergent fibre (NDF)) fed at 0900 h and natural grass hay (6.7% CP, 69.1% NDF) fed ad libitum at 0915 and 1600 h. Mineral salt blocks and water were continuously available. The main objective was to assess seasonal effects on within-day variations in the chewing behaviour of sheep, at small to large time scales within a day. We therefore focused on two experimental periods characterised by contrasting conditions of daylength and temperature (i.e., 'Period 1': 610 min daylight, mean temperature of 10.9 degrees C, and 'Period 4': 550 min daylight, mean temperature of 7.2 degrees C). In particular, differences between periods in the nycterohemeral pattern of chewing behaviour and the quality of forecasts of chewing states were tested. We submitted our data to a new method of analysis that we developed: the method of time-dependent transition probabilities, and compared the results to those obtained using other methods that were available in the literature.Overall, the sheep spent more time eating in Period 1 than in Period 4. Specifically, a secondary peak in eating activity, which was observed in the early afternoon in Period 1, was absent in Period 4. The nycterohemeral pattern of eating activity showed significant differences between periods, at the main rhythmic component of 24 h and at short components around 2 h. Such differences were not observed for ruminating and idling activities. The quality of forecasts of chewing states decreased from Periods 1 to 4, in terms of accuracy (based on R(2)) and lead of reliable forecasts (i.e., &z.sfnc;forecast-observation&z.sfnc;<0.1). The most (least) accurate forecasts were obtained for the ruminating (eating) state in both periods. We have attributed the differences that we found between periods to daylength instead of temperature because the sheep were mostly within the thermoneutral zone in our study. By comparison, using mean hourly times of eating activity, significant differences between periods were detected for the 24-h rhythmic component and the 4-h component, instead of the 2-h component, probably because of aliasing (i.e., when the sampling time interval used is longer than suited; the minute was found to be a suitable interval length in the calculation of time-dependent transition probabilities). Using the age-dependent model of Rook and Penning, minor differences between periods were detected. On that basis, the method of time-dependent transition probabilities may be brought forward as a complement of value to existing methods of behavioural data analysis.

Journal Article↗