Cleaning

Outlier detection is a common problem in data analysis and there are numerous sophisticated methods in existence. We focus here on outliers that are detectable by consideration of physics: time moving backwards, drifters repeating the same pattern endlessly, jumps in position that would only be possible with teleportation. The approach at this step is nondestructive: questionable data are flagged, not removed. The process is based on the data processing steps taken by Hutchings and Martini. In each case, a flag of True means that an problem has been identified.

Basic quality control

>>> import icedrift
>>> data = get_test_data_TBD()
>>> flagged_data = icedrift.check_dates(data)

Simple outlier detection

• Step by step threshold test

• Z-score

Complex outlier detection

These tools use statistical methods to identify outliers.

• LOESS method

• Multi-trajectory approaches