Abstract
This chapter focuses on the Box-Jenkins approach to building models for nonstationary time series. It contains ARIMA modeling for nonseasonal time series presented in Chap. 4 and SARIMA modeling for seasonal time series to be considered in this chapter. Through case study, we demonstrate how to use Python to implement the Box-Jenkins method. In addition, we also discuss REGARMA models.
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References
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Huang, C., Petukhina, A. (2022). Nonstationary Time Series Models. In: Applied Time Series Analysis and Forecasting with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-13584-2_5
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DOI: https://doi.org/10.1007/978-3-031-13584-2_5
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