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In Chapter 10, Building Univariate Time Series Models Using Statistical Methods, you were introduced to popular forecasting techniques such as exponential smoothing, non-seasonal ARIMA, and seasonal ARIMA. These methods, often referred to as classical statistical forecasting approaches, are fast, simple to implement, and easy to interpret.
In this chapter, you will dive head-first and learn about additional statistical methods that build on the foundation you gained from the previous chapter. This chapter will introduce a few libraries that can automate time series forecasting and model optimization - Facebook's (meta) Prophet library. Additionally, you will explore statsmodels' vector autoregressive (VAR) class for working with multivariate time series and the arch library, which supports GARCH for modeling volatility in financial data.
The main goal of this chapter is to familiarize you with automated forecasting...
