Multivariate Time Series Analysis with Deep Learning

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Multivariate Time Series Analysis with Deep Learning"

By

Mr. Shuhan ZHONG


Abstract:

Multivariate time series (MTS) serves as a fundamental data modality across 
numerous science domains and real-world applications, enabling multiple 
analysis tasks. Based on sampling regularity, MTS can be categorized into 
regular (RMTS) and irregular (IMTS) types. Each task and data type presents 
distinct challenges for deep learning models, which must capture complex 
temporal and channel-wise dependencies. This thesis aims to design such deep 
learning models for different MTS analysis scenarios that achieve both 
effectiveness and efficiency.

For RMTS analysis, the core challenge is modeling the complex multi-scale 
temporal compositions. Addressing this challenge, we propose MSD-Mixer, a 
multi-scale decomposition MLP-Mixer, which learns to explicitly decompose the 
input RMTS into multi-scale temporal patches for modeling. Each layer employs 
specialized multi-layer perceptrons to mix intra-patch, inter-patch, and 
cross-channel features, while a novel residual loss ensures decomposition 
completeness. MSD-Mixer beats state-of-the-art performance across forecasting, 
imputation, anomaly detection, and classification tasks.

For IMTS classification, the primary obstacle is the severe channel-wise 
asynchrony, which cripples standard attention mechanism. Overcoming this 
limitation, we propose MTM, a multi-scale token mixing Transformer, which 
mitigates the asynchrony via multi- scale down-sampling and introduces a 
proactive token mixing mechanism that identifies and exchanges pivotal tokens 
across unsynchronized channels, enabling superior IMTS classification accuracy 
over existing methods.

For IMTS forecasting, where existing methods trade off between effectiveness 
and efficiency, we propose EMIRA. It extends the efficient Mamba model for 
IMTS forecasting with two key innovations: a context-aware selectivity that 
integrates global irregularity context into the scan process, and a query 
prompting mechanism that uses forecasting queries to proactively retrieve 
relevant observations. EMIRA outperforms existing methods in both forecasting 
accuracy and efficiency.


Date:                   Wednesday, 11 March 2026

Time:                   3:00pm - 5:00pm

Venue:                  Room 2612A
                        Lifts 31-32

Chairman:               Prof. David COOK (ECON)

Committee Members:      Prof. Gary CHAN (Supervisor)
                        Prof. Andrew HORNER
                        Prof. Xiaofang ZHOU
                        Dr. Sisi JIAN (CIVL)
                        Dr. Wentao CHENG (BNBU)