Qualitative Analysis of Anomaly Detection in Time Series

• Published in: 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) pp. 250 - 253
• Main Authors: Bhat, Mohammad Qazim, Alex, Sini Anna, Nanda, Simran, Goutham, Sevanthi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21-12-2022
• Subjects: alert system; anomaly detection; automated machine learning; data mining; Ergonomics; Feature extraction; Forestry; interpretable machine learning; payment fraud detection; Prediction algorithms; Predictive models; Time series analysis; unsupervised learning; Weather forecasting
• DOI: 10.1109/I4C57141.2022.10057732

We present an end-end system for time series anomaly detection specifically aimed at detecting fraudulent transactions in bank transaction datasets. The goal of anomaly detection is to locate unusual or uncommon occurrences in data. Outlier detection, one of the most crucial data analysis jobs, has many practical uses. For instance, in order to track their company data and send out notifications for outliers, Yahoo and Microsoft have developed their own services for time-series outlier identification. However, majority of outlier identification methods employ classifications of unusual entries without providing any justification. Therefore, the goal is to make the anomalous points as predicted by the model more interpretable and thereby create a flagging system to help human labelers in determining anomalous behavior. This will help reduce human engineering efforts by a huge margin.