Anomaly Detection - Time Series Outlier Detection

Time Series Outlier Detection

Parametric tests to find outliers in time series are implemented in almost all statistical packages: Demetra+, for example, uses the most popular ones. One way to detect anomalies in time series is a simple non parametric method called washer. It uses a non parametric test to find one or more outliers in a group of even very short time series. The group must have a similar behaviour, as explained more fully below. An example is that of municipalities cited in the work of Dahlberg and Johanssen (2000). Swedish municipalities expenditures between 1979 and 1987 represent 256 time series. If you consider three years such as, for example, 1981,1982 and 1983, you have 256 simple polygonal chains made of two lines segments. Every couple of segments can approximate a straight line or a convex downward (or convex upward) simple polygonal chain. The idea is to find outliers among the couples of segments that performs in a too much different way from the other couples. In the washer procedure every couple of segments is represented by an index and a non parametric test (Sprent test ) is applied to the unknown distribution of those indices. For implementing washer methodology you can download an open source R (programming language) function with a simple numeric example.