ABSTRACT
This chapter focuses on ways of understanding typical time-series effects, such as trend, seasonality, and correlations between successive observations. Statistical techniques for analysing time series range from relatively straightforward descriptive methods to sophisticated inferential techniques. Traditional methods of time-series analysis are mainly concerned with decomposing the variation in a series into components representing trend, seasonal variation and other cyclic changes. Much of the probability theory of time series is concerned with stationary time series, and for this reason time series analysis often requires one to transform a non-stationary series into a stationary one so as to use probability theory. The first, and most important, step in any time-series analysis is to plot the observations against time. The chapter provides some important comments on how to handle real data. Analysts generally like to think they have ‘good’ data, meaning that the data have been carefully collected with no outliers or missing values.
