Spatio-Temporal Methods in Environmental Epidemiology
Chapter 10 - Why Time also Matters
Summary
The chapter contains the theory required for handling time series data. From this chapter, the reader will have gained an
understanding of the following topics:
That a temporal process consists of both low and high frequency components, the former playing a key role in
determining
long-term trends while the latter may be associated with shorter-term changes.
Techniques for the exploratory analysis of the data generated by the temporal process, including the ACF (correlogram)
and PACF (periodogram).
Models for irregular (high frequency) components after the regular components (trend) have been removed.
Methods for forecasting, including exponential smoothing and ARIMA modelling.
The state space modelling approach, which sits naturally within a Bayesian setting and which provides a general
framework for most of the classical time series models and many more besides.
Implementing time series processes within a Bayesian hierarchical framework.
R CODE
Example 10.1
Example 10.2
Example 10.13
Example 10.14
Example 10.16
Example 10.17
Kings Death Data Analysis
DATA
Los Angeles hourly ozone data
Los Angeles hourly ozone data-specific site
Other
Supplementary Material