Abstract: Techniques for implementing unsupervised universal anomaly detection for situation handling are disclosed. In some example embodiments, a computer-implemented method comprises detecting an anomaly in a new data point that has corresponding manifestation values for variable categories based on a restriction index for the corresponding manifestation value for at least one of the variable categories in the new data point, and causing a notification of the anomaly in the new data point to be displayed on a computing device based on the detecting of the anomaly. The restriction index for the corresponding manifestation value for the at least one of the variable categories in the new data point may be calculated for the corresponding manifestation value for each other variable category in the plurality of variable categories based on a manifestation space value and a prediction space value that are based on historical data points.

Abstract: Techniques for implementing unsupervised universal anomaly detection for situation handling are disclosed. In some example embodiments, a computer-implemented method comprises detecting an anomaly in a new data point that has corresponding manifestation values for variable categories based on a restriction index for the corresponding manifestation value for at least one of the variable categories in the new data point, and causing a notification of the anomaly in the new data point to be displayed on a computing device based on the detecting of the anomaly. The restriction index for the corresponding manifestation value for the at least one of the variable categories in the new data point may be calculated for the corresponding manifestation value for each other variable category in the plurality of variable categories based on a manifestation space value and a prediction space value that are based on historical data points.