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Automatic ARIMA!
Automatic model selection and multi-step forecasting
ARIMA (AutoRegressive Integrated Moving Average) is a statistical model used for time series forecasting and analysis. The origin of ARIMA can be traced back to the early 1900s with the development of autoregressive (AR) models and moving average (MA) models separately. Neither model appears sufficient to capture the complex dynamics of real-world time series data. In the 1960s, the AR and MA models were formally united into ARIMA by three statisticians: George E. P. Box, Gwilym M. Jenkins, and Gregory C. Reinsel in their book “Time Series Analysis: Forecasting and Control” [1].
Since ARIMA probably is the well-known paradigm, why do I still include it in this “modern” time series book? The primary reason is that AR and MA leave many footprints in many modern time series techniques. A fundamental understanding of ARIMA helps us to leverage to other complex models. For example, we have seen the AR module in Chapter 4 NeuralProphet, And we will see the AR terms as the features in supervised-learning models in Chapter 12 and 13. The second reason to include ARIMA in this book is the recent code development enabling automatic model selection and multi-period forecasting.
