Anomaly detection identifies data points, events and observations that deviate from the normal behavior of the data set. Anomalous data can indicate critical incidents, such as a change in consumer behavior or observations that are suspicious. Anomalies in data are also called standard deviations, outliers, noise, novelties, and exceptions.
TD_IQR uses interquartile range for anomaly detection. Any data point that falls outside of 1.5 times of an interquartile range below the first quartile and above the third quartile is considered an outlier.