Abstract
An important goal of time series analysis is forecasting. In the following we will consider the problem of forecasting X T+h , h > 0, given {X T , …, X 1} where {X t } is a stationary stochastic process with known mean μ and known autocovariance function γ(h). In practical applications μ and γ are unknown so that we must replace these entities by their estimates. These estimates can be obtained directly from the data as explained in Sect. 4.2 or indirectly by first estimating an appropriate ARMA model (see Chap. 5) and then inferring the corresponding autocovariance function using one of the methods explained in Sect. 2.4 Thus the forecasting problem is inherently linked to the problem of identifying an appropriate ARMA model from the data (see Deistler and Neusser 2012).
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- 1.
Elliott and Timmermann (2008) provide a general overview of forecasting procedures and their evaluations.
- 2.
Note the similarity of the forecast errors with the least-square residuals of a linear regression.
- 3.
See Brockwell and Davis (1991, p. 167).
- 4.
More about harmonic processes can be found in Sect. 6.2
- 5.
The Wold Decomposition corresponds to the decomposition of the spectral distribution function of F into the sum of F Z and F V (see Sect. 6.2). Thereby the spectral distribution function F Z has spectral density \(f_{Z}(\lambda ) = \tfrac{\sigma ^{2}} {2\pi } \vert \Psi (e^{-\imath \lambda })\vert ^{2}\).
- 6.
The series \(\psi _{j} = 1/j\), for example, is square summable, but not absolutely summable.
- 7.
This happens, for example, when many, perhaps thousands of time series have to be forecasted in a real time situation.
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Neusser, K. (2016). Forecasting Stationary Processes. In: Time Series Econometrics. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-32862-1_3
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