Reading Group

Speaker: Kes Ward, Lancaster University

Title: iForest, a point anomaly detection method useful for streaming data​​

Abstract: ​​We'll be covering iForest, a point anomaly detection method useful for streaming data. The original iForest paper (2008) can be found here. Physical copies may be available at the reading group. There will be a short presentation by Kes covering the necessary background, followed by an interactive discussion in small groups, followed by feeding back to the main group.

Key concepts to discuss: What methodological tricks make iForest work? How has iForest been extended and its weaknesses addressed/adapted to other settings? How has iForest gone from a research paper to being successfully and impactfully implemented in practice? What can we learn from all this that's useful for our own research?

​Speaker: Han Yan, London School of Economics and Political Science​

Title: Introduction to Transformer and Its Applications in Time Series Anomaly Detection

Abstract:​ This presentation provides an overview of the Transformer architecture, highlighting its core self-attention mechanism. We will explore how it enables the model to effectively capture long-range dependencies in sequential data. The discussion will then shift to its application in time-series and anomaly detection. We will examine how the Anomaly Transformer conducts unsupervised anomaly detection based on Anomaly-Attention.

​Speaker: Zetai Cen, University of Bristol​

Title: Outlier detection in multivariate time series

Abstract: We provide a brief overview on outliers in multivariate time series, as a generalization from univariate to low-dimensional, and to briefly appreciate such a problem in high-dimensional context. Built on vector autoregressive and moving-average models, we show how certain model structures can be exploited to facilitate outlier detection. By no means our discussion will be complete, yet it should provide us more insight on outliers in complex data streams, and even so if anomaly is the object of interest.

Speaker: Gengyu Xue​

Title: Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network​

Abstract: We will go through the paper “Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network” (link). We will start with a brief introduction to the key concepts used in the paper: Gated Recurrent Unit (GRU), Variational Autoencoder (VAE) and Planar Normalising Flows. We will then explore the network architecture of OmniAnomaly and discuss how the method effectively detects anomalies as well as anomaly interpretation. The session will conclude with an open discussion and feedback from the audience.

Speaker: TBC​

Title: TBC​

Abstract: TBC