Chapter 3 Time series components and simple forecasting methods
Before we turn to the state space framework, ETS, ARIMA, and other models, we need to discuss time series decomposition and the ETS taxonomy. These topics lie at the heart of ETS models and are essential for understanding further material.
In this chapter, we start with a discussion of time series components, then move to the idea of decomposing time series into distinct components and applying simple forecasting methods, including Naïve, Global Average, Simple Moving Average, and Simple Exponential Smoothing.
The software behind this book. The methods described here are implemented in the smooth and greybox packages for R and Python, which are free and open source.
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