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