Running summarizing tools on a new article

Abstract: This chapter helps the readers to discover a suite of classical methods for time series forecasting that they can test on their forecasting problems. It is structured to give the readers just enough information on each method to get started with a working code example and where to look to get more information on the method. The chapter looks at the following classical methods: autoregression, moving average, autoregressive moving average (ARMA), autoregressive integrated moving average, and automated machine learning. It explores how to implement the autoregression for time series forecasting with Python. The chapter also helps the readers to discover the best tools to develop and implement a moving average model with Python. ARMA models always had a key role in the modeling of time series, as their linear structure contributes to a substantial simplification of linear prediction.