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Predictive Time Series Modeling

In: Time Series Forecasting using Machine Learning

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  • Tsung-wu Ho

    (National Taiwan Normal University)

Abstract

This chapter shows the ways to implement statistical prediction. The starting point for statistical time series forecasting is ARIMA, we introduce the automatic order selection function auto.arima(), a routine offered by package forecast. Besides ARIMA, this chapter also includes several nonlinear time series models, for example, self-exciting threshold autoregression. We also detail the procedure to generate multistep forecasts and onestep ahead forecast.

Suggested Citation

  • Tsung-wu Ho, 2025. "Predictive Time Series Modeling," Springer Books, in: Time Series Forecasting using Machine Learning, chapter 0, pages 19-57, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-97946-0_2
    DOI: 10.1007/978-3-031-97946-0_2
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