Deep learning techniques for anomaly detection in time series data using transformer - Chan, Rachel Si Min (Nanyang Technological University, 2022)
Due to the growing demand and applications in many fields producing massive amounts of high dimensional data, anomaly detection is becoming increasingly important. The correlation between sequences makes multivariate anomalies more difficult on top of univariate anomalies. With the proliferation of data modalities, the issue of anomaly detection in large-scale databases is getting harder. Therefore, the main objective of this project will be to identify irregularities in multivariate time series data using deep learning techniques.