Abstract
This chapter introduces time series models which are most commonly used in statistical forecasting: regression models, including trend models, stationary time series models, the ARIMA(p, d, q) model, nonlinear models, multivariate time series models (including VARMA(p, q) and simultaneous equations models), as well as models of discrete time series with a specific focus on high-order Markov chains.
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Kharin, Y. (2013). Time Series Models of Statistical Forecasting. In: Robustness in Statistical Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-319-00840-0_3
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DOI: https://doi.org/10.1007/978-3-319-00840-0_3
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