**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