Abstract
This chapter introduces basic concepts such as time series, stationary process and covariance function. Subsequently, the time domain of a stationary process, which is a subspace of the Hilbert space of square integrable random variables, is presented. This Hilbert space approach allows for nice geometric interpretations and offers useful tools e.g. for prediction. The penultimate section describes important classes of stationary processes: white noise, moving average processes, autoregressive processes and harmonic processes. The final section discusses examples of non-stationary processes.
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