Dealing with outliers

Time series data often exhibit seasonal patterns (for example, sales spikes during holidays) and trends (for example, gradual growth over the years). An outlier in this context might not be an anomaly; rather, it could reflect a normal seasonal effect or a change in the underlying trend. For example, a sudden spike in retail sales during Black Friday is expected and should not be treated as an outlier. Techniques such as seasonal decomposition of time series (STL), autocorrelation, and seasonal indices can aid in understanding the expected behavior of the data, thus providing a clearer basis for identifying outliers.

Identifying outliers with seasonal decomposition

One way to identify outliers in time series is to decompose the series into trend, seasonality, and residual components, as outliers are often identified in the residual component. To decompose the series into trend, seasonality, and residual components, we can use the STL method. This method...