This chapter highlights fundamental time series concepts, as well as features and models that are relevant to the clustering and classification of time series. A Stochastic process is defined as a collection of random variables that are ordered in time and defined as a set of points which may be discrete or continuous. Most statistical problems are concerned with estimating the properties of a population from a sample. The properties of the sample are typically determined by the researcher, including the sample size and whether randomness is incorporated into the selection process. A Moving average (MA) model is one where the current value of the deviation of the process from the mean is expressed as a linear combination of a finite number of previous error terms. Many time series encountered in various fields exhibit non-stationary behaviour and in particular they do not fluctuate about a fixed level.