ANOMALY DETECTION AND ATTRIBUTION USING AUTO FORECAST AND DIRECTED GRAPHS

Abstract

In the business world, decision makers rely heavily on data to back their decisions. With the quantum of data increasing rapidly, traditional methods used to generate insights from reports and dashboards will soon become intractable. This creates a need for efficient systems which can substitute human intelligence and reduce time latency in decision making. This paper describes an approach to process time series data with multiple dimensions such as geographies, verticals, products, efficiently, and to detect anomalies in the data and further, to explain potential reasons for the occurrence of the anomalies. The algorithm implements auto selection of forecast models to make reliable forecasts and detect such anomalies. Depth First Search (DFS) is applied to analyse each of these anomalies and find its root causes. The algorithm filters the redundant causes and reports the insights to the stakeholders. Apart from being a hair-trigger KPI tracking mechanism, this algorithm can also be customized for problems lke A/B testing, campaign tracking and product evaluations.