Titlebook: Introduction to Time Series and Forecasting; Peter J. Brockwell,Richard A. Davis Textbook 2016Latest edition Springer Nature Switzerland A

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Springer Nature Switzerland AG 2016

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ARMA Models,In this chapter we introduce an important parametric family of stationary time series, the autoregressive moving-average, or ARMA, processes. For a large class of autocovariance functions .(??) it is possible to find an ARMA process {..} with ACVF ..(??) such that .(??) is well approximated by ..(??).

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Nonstationary and Seasonal Time Series Models,In this chapter we shall examine the problem of finding an appropriate model for a given set of observations {..,?.,?..} that are not necessarily generated by a stationary time series.

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Multivariate Time Series,ependence within each component series {..} but also interdependence between the different component series {..} and {..}, . ≠ .. Much of the theory of univariate time series extends in a natural way to the multivariate case; however, new problems arise.

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Peter J. Brockwell,Richard A. DavisDesigned for use in full-year courses introducing univariate and multivariate time series and forecasting at the advanced undergraduate and graduate levels.Exercise problems at the end of each chapter

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Spectral Analysis,ess, which for some applications may be more illuminating. For example, in the design of a structure subject to a randomly fluctuating load, it is important to be aware of the presence in the loading force of a large sinusoidal component with a particular frequency to ensure that this is not a reson