Anomaly detection
Team members: Andreas Wallstrom, Karen Kauffman, Reesa John, Kedwin Chen
Technologies used: Python and R
With the python code we are detecting outliers by looking at the rate of change for between a set of points applying a low-pass filter.
Outliers detected
We have many more screenshots in the Screenshot folder.
The Y axis is the SUM() of delivered_price. The X axis is the time, grouped by week.