Henrik Boström

Addressing Methodological Challenges in Time Series Anomaly Detection

Time series anomaly detection focuses on detecting unexpected events in time series data. Traditional techniques for this task include SVMs, k-Nearest Neighbors and PCA, while more recent approaches frequently employ deep learning, e.g., using variational autoencoders and graph neural networks. In several studies, thes...