Tutorial: Deep Learning Methods for Unsupervised Time Series Anomaly Detection

Abstract

Time series anomaly detection (TSAD) is a crucial tool for detecting unusual patterns or outliers in large-scale time series datasets. With the increasing amount of data being generated in various industries, from financial transactions to industrial processes, it has become essential to identify anomalies that may indicate unusual events, fraud, or errors in the system. Deep learning algorithms have proven to be effective in detecting these anomalies, as they can learn complex patterns and relationships in the data that may not be apparent to humans. By detecting anomalies early, businesses can take proactive measures to prevent or minimize their impact, improving their overall performance and reducing risk. Thus, deep TSAD is an important tool for ensuring large-scale systems’ quality, reliability, and security and has attracted huge interest from the research community. This tutorial aims to provide the audience with a comprehensive and organized tutorial on state-of-the-art algorithms for time-series anomaly detection. We will first discuss the basic concepts of anomaly detection and time-series processing. Then, we will thoroughly review the deep learning methods for time-series anomaly detection, ranging from traditional autoencoder-based algorithms to the recently-proposed graph-based models. In the final parts of this tutorial, we will focus on some important considerations during the implementation, evaluation and dataset preparation for the deep time-series anomaly detection algorithms. Besides introducing the SOTA methods, this tutorial will aim to enable the researchers to apply them to solve real-world problems involving time series.

Narges Armanfard (Chair)

Associate Professor

Narges Armanfard is an Associate Professor of Electrical and Computer Engineering at McGill University and Mila - Quebec AI Institute. She is also affiliated with McGill Centre for Intelligent Machines (CIM), McGill initiative in Computational Medicine (MiCM) and McGill Institute for Aerospace Engineering (MIAE). Dr. Armanfard is the founder and principal investigator of the iSMART Lab with the mission to pioneer advanced algorithms in artificial intelligence, with expertise in computer vision, time series analysis, tabular data, large language models, and visual language models.

Dimitrios Sinodinos

AI Researcher (PhD)

Dimitrios completed his Bachelor’s degree in Electrical Engineering at McGill University and was awarded the British Association Medal for graduating with the highest CGPA in the department. Shortly after starting his Master’s, Dimitrios fast-tracked into a PhD, where he received the MEDA Award and was selected into the prestigious Vadasz Scholars program. His research involves multi-task learning learning employing lightweight multimodal large language models.

Hadi Hojjati

AI Researcher (PhD)

Hadi received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology, Iran. He started as an MSc student at the iSMART Lab. and fast-tracked to PhD. He has received Graduate Excellence Fellowship Award (GEF), McGill Engineering Doctoral Award (MEDA), GREAT Award, and AGE-WELL Award. He is currently doing research on multi-modal and multi-variate anomaly detection in time-series data.

Thi Kieu Khanh Ho

AI Researcher (PhD)

Khanh received her Master’s degree at Gwangju Institute of Science and Technology (GIST), South Korea. She was a researcher at Seoul National University Hospital (SNUH), South Korea, before moving to Montreal for her PhD. She has received the prestigious McGill Engineering Doctoral Award (MEDA), GREAT Award and is nominated for the prestigious Vanier Scholarship. Her research focuses on time-series data anomaly detection using generative models and graphs.

Thomas Lai

AI Researcher (PhD)

Thomas received his Bachelor of Engineering (B.Eng.) from McGill University. A year after starting his Master’s at the iSMART Lab, Thomas fast-tracked into a PhD. He is a recipient of the MEUSMA and FRQ awards. His research is on open-set anomaly detection.

Alexander Koran

AI Researcher (MSc)

Alex grew up near Boston, and he moved to Montreal after finishing high school to pursue his undergraduate studies at McGill. He completed his Bachelor’s Degree in Computer Engineering with a math minor and an AI minor in Fall 2023. Some of his research interests are algorithm design, machine learning applications, and quantum computing. He is now pursuing a Master’s of Science at the iSMART lab and is a recipient of the GEF award. His MSc thesis focuses on time series anomaly detection.

Jack Wei

AI Researcher (MSc)

Jack (Yi) Wei received his Bachelor’s degree in Computer Engineering with a minor in Mathematics from McGill University. He is the recipient of the McGill Engineering Undergraduate Student Award (MEUSMA). His interest lies in tabular data analysis.

Zihan Wang

AI Researcher (MSc)

Zihan received his Bachelor’s degree in Electrical and Computer Engineering from the University of Alberta, Canada. He is now an MSc student at the iSMART Lab. He is the recipient of the Graduate Excellence Fellowship Award. His research at the iSMART Lab is focused on continual learning with large language models for anomaly detection.