Key result
Novel time-series models capture non-linear trends and seasonal patterns in coronary heart disease events.
Observational
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We present two methods of estimating the trend, seasonality and noise in time series of coronary heart disease events. In contrast to previous work we use a non-linear trend, allow multiple seasonal components, and carefully examine the residuals from the fitted model. We show the importance of estimating these three aspects of the observed data to aid insight of the underlying process, although our major focus is on the seasonal components. For one method we allow the seasonal effects to vary over time and show how this helps the understanding of the association between coronary heart disease and varying temperature patterns.
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Barnett et al. (2004) conducted an observational in Coronary heart disease. Seasonality and varying temperature patterns was evaluated on Coronary heart disease events. Two time-series modeling methods incorporating non-linear trends and time-varying multiple seasonal components were presented to estimate coronary heart disease event patterns.
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