PhD Thesis Proposal - Arash Hajisafi

Presentation Title: Dynamic GNNs for Accurate and Efficient Modeling of Instant and Lagged Dependencies in Multivariate Time Series
 
Date and Time: Wednesday, April 9th, 2025 - 12:30p - 2:00p
 
Location: GCS 502C
 
Committee Members: Cyrus Shahabi (Chair), Ibrahim Sabek, Viktor Prasanna, Ruishan Liu, John P. Wilson (External)
 
Abstract: Graph Neural Networks (GNNs) have shown great success in modeling complex dependencies within multivariate time series by explicitly capturing intra-series (within individual series) and inter-series (across different series) relationships. However, existing methods often struggle to represent evolving correlations, particularly when multiple contexts and lagged interactions are involved. My previous research has developed GNN-based prediction models addressing instant dependencies across various contexts, incorporating both static and dynamic relationship aspects, and achieving significant improvements in forecasting accuracy and efficiency. Despite these advancements, real-world time series, such as those found in financial markets, frequently exhibit lagged dependencies, where changes in one series influence others after varying delays. Building on my prior contributions, my dissertation proposes developing a novel dynamic GNN method explicitly designed to capture these lagged dependencies, aiming to further enhance the prediction accuracy in applications like stock forecasting.

Location: Ginsburg Hall (GCS) - 502C

Audiences: Everyone Is Invited

Contact: Arash Hajisafi

This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.

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