Detecting and treating outliers
An outlier, as its name suggests, is an observation that lies at an abnormal distance from the rest of the observations. If we are looking at a data generating process (DGP) as a stochastic process that generates the time series, the outliers are the points that have the least probability of being generated from the DGP. This can be for many reasons, including faulty measurement equipment, incorrect data entry, and black-swan events, to name a few. Being able to detect such outliers and treat them may help your forecasting model understand the data better.
Outlier/anomaly detection is a specialized field itself in time series, but in this book, we are going to restrict ourselves to simpler techniques of identifying and treating outliers. This is because our main aim is not to detect outliers, but to clean the data for our forecasting models to perform better. If you want to learn more about anomaly detection, head over to the Further reading section...
