Time Series Regression (time + series_regression)

Distribution by Scientific Domains

Selected Abstracts

On case-crossover methods for environmental time series data

Heather J. Whitaker
Abstract Case-crossover methods are widely used for analysing data on the association between health events and environmental exposures. In recent years, several approaches to choosing referent periods have been suggested, with much discussion of two types of bias: bias due to temporal trends, and overlap bias. In the present paper, we revisit the case-crossover method, focusing on its origin in the case-control paradigm, in order to throw new light on these biases. We emphasise the distinction between methods based on case-control logic (such as the symmetric bi-directional (SBI) method), for which overlap bias is a consequence of non-exchangeability of the exposure series, and methods based on cohort logic (such as the time-stratified (TS) method), for which overlap bias does not arise. We show by example that the TS method may suffer severe bias from residual seasonality. This method can be extended to control for seasonality. However, time series regression is more flexible than case-crossover methods for the analysis of data on shared environmental exposures. We conclude that time series regression ought to be adopted as the method of choice in such applications. Copyright 2006 John Wiley & Sons, Ltd. [source]

Forecasting Daily Patient Volumes in the Emergency Department

Spencer 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]

Blockwise empirical entropy tests for time series regressions

Francesco Bravo
Abstract., This paper shows how the empirical entropy (also known as exponential likelihood or non-parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series. Monte Carlo evidence suggests that the proposed test statistics have better finite-sample properties than conventional test statistics such as the Wald statistic. [source]