A Survey on Deep Learning Models for Time Series Analysis

PhD Qualifying Examination


Title: "A Survey on Deep Learning Models for Time Series Analysis"

by

Mr. Shuhan ZHONG


Abstract:

Time series analysis, a long-standing area of research, involves processing 
sequential data points collected over time to extract meaningful information 
for tasks such as forecasting, classification, imputation, and anomaly 
detection. Unlike image or text data, time series data is characterized by its 
complex temporal variations, posing unique challenges in identifying the 
underlying patterns. Classical time series methods have been instrumental in 
modeling these patterns but struggle with scalability and expressiveness in the 
face of large multivariate time series datasets. To this end, much effort has 
been made to apply deep learning methods for time series analysis, leading to a 
surge in related publications, which makes it challenging to keep pace with the 
developments in this field.

This survey aims to provide a comprehensive overview of deep learning models 
for time series analysis across different tasks. Specifically, we review recent 
publications in this area and provide a taxonomy that groups diverse and 
relevant papers into categories by how they integrate classical time series 
methods into deep learning models, and how they innovate existing deep 
architectures. We present the underlying principles and discuss representative 
works. Besides, we summarize the most widely-used benchmarking datasets and 
protocols for different tasks with a quantitative evaluation of representative 
models. We then highlight key challenges and promising future research 
directions in this area.


Date:                   Wednesday, 20 November 2024

Time:                   4:00pm - 6:00pm

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Prof. Gary Chan (Supervisor)
                        Prof. Qiong Luo (Chairperson)
                        Prof. Andrew Horner
                        Prof. Xiaofang Zhou