ABSTRACT
This chapter focuses on various types of time-series models that collectively come under the general title of ‘stochastic processes’. An important class of stochastic processes are those that are stationary. The chapter investigates the general properties of the autocorrelation function (ac.f.). Autoregressive moving average (ARMA) model A useful class of models for time series is formed by combining moving average (MA) and autoregressive (AR) processes. The importance of ARMA processes lies in the fact that a stationary time series may often be adequately modelled by an ARMA model involving fewer parameters than a pure MA or AR process by itself. The ac.f. of the general ARMA process can be found using similar procedures as for AR processes. The chapter provides a brief introduction to processes in continuous time. It is mainly concerned with indicating some of the mathematical problems that arise when time is continuous.
