In this final chapter we touch on a variety of topics of special interest. In Section 10.1 we consider transfer function models, designed to exploit for predictive purposes the relationship between two time series when one acts as a leading indicator for the other. Section 10.2 deals with intervention analysis, which allows for possible changes in the mechanism generating a time series, causing it to have different properties over different time intervals. In Section 10.3 we introduce the very fast growing area of nonlinear time series analysis, and in Section 10.4 we briefly discuss continuous-time ARMA processes, which, besides being of interest in their own right, are very useful also for modelling irregularly spaced data. In Section 10.5 we discuss fractionally integrated ARMA processes, sometimes called “long-memory” processes on account of the slow rate of convergence of their autocorrelation functions to zero as the lag increases.
KeywordsTransfer Function Model Autocovariance Function ARMA Process Nonlinear Time Series Analysis White Noise Sequence
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