In this post, we’ll explore:
- Different types of time series outliers
- Prediction-based and estimation-based methods for detecting outliers
- How to deal with unwanted outliers using replacement
Outliers are observations that deviate significantly from normal behavior.
Time series can exhibit outliers due to some unusual and non-repetitive event. These affect time series analysis and mislead practitioners into erroneous conclusions or defective forecasts. So, identifying and dealing with outliers is a key step to ensure a reliable time series modelling.
In time series, outliers are usually split into two types: additive outliers and innovational outliers.
Additive Outliers
An additive outlier is an observation that exhibits an unusually high (or low) value relative to historical data.
An example of an additive outlier is the surge in the sales of a product due to a promotion or related viral content. Sometimes these outliers occur due to erroneous data collection. The additivity has to do…