This week, we go over time series smoothing functions, highlight its assumptions and parameters, and demonstrate its application through examples.
Why should we care? Smoothing is very often used (and abused) in the industry to make a quick visual examination of the data properties (e.g. trend, seasonality, etc.), fit in missing values, and conduct a quick out-of-sample forecast.
In this issue, we will discuss five (5) different smoothing methods: weighted moving average (WMA), simple exponential smoothing, double exponential smoothing, linear exponential smoothing, and triple exponential smoothing.
