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Autoregressive Integrated Moving Average (autoregressive + integrate_moving_average)
Selected AbstractsAll-cause mortality and fatal alcohol poisoning in Belarus, 1970,2005DRUG AND ALCOHOL REVIEW, Issue 5 2008YURY E. RAZVODOVSKY Abstract Introduction and Aims. Although alcohol appears to be an important contributor to the burden of disease in the countries of eastern Europe, little systematic research has been undertaken on its impact on mortality in the former Soviet republic of Belarus. There may be a number of factors underlying the particularly negative effect of alcohol on mortality in Belarus, including the pattern of drinking and use of surrogates. A solid body of research and empirical evidence suggests that hazardous patterns of alcohol consumption (binge drinking) lead to quicker and deeper intoxication, increasing the propensity for alcohol-related mortality. Design and Method. To estimate the aggregate level effect of binge drinking on the all-cause mortality rate, trends in the all-cause mortality and fatal alcohol poisoning rates (as a proxy for binge drinking) in Belarus from 1970 to 2005 were analysed employing AutoRegressive Integrated Moving Average (ARIMA) time,series analysis in order to assess a bivariate relationship between the two time,series. Results. The results of time,series analysis suggest a close relationship between all-cause mortality and fatal alcohol poisoning rates at the population level. Conclusions. This study supports the hypothesis that alcohol and all-cause mortality are connected closely in countries where the drinking culture is characterised by heavy drinking episodes and adds to the growing body of evidence that a substantial proportion of total mortality in Belarus is due to acute effects of binge drinking. [source] Impact of US and Canadian precursor regulation on methamphetamine purity in the United StatesADDICTION, Issue 3 2009James K. Cunningham ABSTRACT Aims Reducing drug purity is a major, but largely unstudied, goal of drug suppression. This study examines whether US methamphetamine purity was impacted by the suppression policy of US and Canadian precursor chemical regulation. Design Autoregressive integrated moving average (ARIMA)-intervention time,series analysis. Setting Continental United States and Hawaii (1985,May 2005). Interventions US federal regulations targeting precursors, ephedrine and pseudoephedrine, in forms used by large-scale producers were implemented in November 1989, August 1995 and October 1997. US regulations targeting precursors in forms used by small-scale producers (e.g. over-the-counter medications) were implemented in October 1996 and October 2001. Canada implemented federal precursor regulations in January 2003 and July 2003 and an essential chemical (e.g. acetone) regulation in January 2004. Measurements Monthly median methamphetamine purity series. Findings US regulations targeting large-scale producers were associated with purity declines of 16,67 points; those targeting small-scale producers had little or no impact. Canada's precursor regulations were associated with purity increases of 13,15 points, while its essential chemical regulation was associated with a 13-point decrease. Hawaii's purity was consistently high, and appeared to vary little with the 1990s/2000s regulations. Conclusions US precursor regulations targeting large-scale producers were associated with substantial decreases in continental US methamphetamine purity, while regulations targeting over-the-counter medications had little or no impact. Canada's essential chemical regulation was also associated with a decrease in continental US purity. However, Canada's precursor regulations were associated with purity increases: these regulations may have impacted primarily producers of lower-quality methamphetamine, leaving higher-purity methamphetamine on the market by default. Hawaii's well-known preference for ,ice' (high-purity methamphetamine) may have helped to constrain purity there to a high, attenuated range, possibly limiting its sensitivity to precursor regulation. [source] Combining wavelet-based feature extractions with relevance vector machines for stock index forecastingEXPERT SYSTEMS, Issue 2 2008Shian-Chang Huang Abstract: The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced. [source] Time series forecasting by combining the radial basis function network and the self-organizing mapHYDROLOGICAL PROCESSES, Issue 10 2005Gwo-Fong Lin Abstract Based on a combination of a radial basis function network (RBFN) and a self-organizing map (SOM), a time-series forecasting model is proposed. Traditionally, the positioning of the radial basis centres is a crucial problem for the RBFN. In the proposed model, an SOM is used to construct the two-dimensional feature map from which the number of clusters (i.e. the number of hidden units in the RBFN) can be figured out directly by eye, and then the radial basis centres can be determined easily. The proposed model is examined using simulated time series data. The results demonstrate that the proposed RBFN is more competent in modelling and forecasting time series than an autoregressive integrated moving average (ARIMA) model. Finally, the proposed model is applied to actual groundwater head data. It is found that the proposed model can forecast more precisely than the ARIMA model. For time series forecasting, the proposed model is recommended as an alternative to the existing method, because it has a simple structure and can produce reasonable forecasts. Copyright © 2005 John Wiley & Sons, Ltd. [source] Asymptotic self-similarity and wavelet estimation for long-range dependent fractional autoregressive integrated moving average time series with stable innovationsJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2005Stilian Stoev Primary 60G18; 60E07; Secondary 62M10; 63G20 Abstract., Methods for parameter estimation in the presence of long-range dependence and heavy tails are scarce. Fractional autoregressive integrated moving average (FARIMA) time series for positive values of the fractional differencing exponent d can be used to model long-range dependence in the case of heavy-tailed distributions. In this paper, we focus on the estimation of the Hurst parameter H = d + 1/, for long-range dependent FARIMA time series with symmetric , -stable (1 < , < 2) innovations. We establish the consistency and the asymptotic normality of two types of wavelet estimators of the parameter H. We do so by exploiting the fact that the integrated series is asymptotically self-similar with parameter H. When the parameter , is known, we also obtain consistent and asymptotically normal estimators for the fractional differencing exponent d = H , 1/,. Our results hold for a larger class of causal linear processes with stable symmetric innovations. As the wavelet-based estimation method used here is semi-parametric, it allows for a more robust treatment of long-range dependent data than parametric methods. [source] Forecasting Models of Emergency Department CrowdingACADEMIC EMERGENCY MEDICINE, Issue 4 2009Lisa M. Schweigler MD Abstract Objectives:, The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. Methods:, From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. Results:, The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. Conclusions:, Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours. [source] Forecasting Daily Patient Volumes in the Emergency DepartmentACADEMIC EMERGENCY MEDICINE, Issue 2 2008Spencer S. Jones MStat Abstract Background:, Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. Objectives:, The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Methods:, Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. Results:, All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, postsample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of postsample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. Conclusions:, This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes. [source] | |||||||||||||