activity
20242026
most citedEnhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI2026

A time-series classification framework for individual-level absenteeism prediction under severe class imbalance

Kwong Ho Li, Matthew Roughan, Wathsala Karunarathne

Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and deli…

cs.CL2026

Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts

Yi Ren, Lewis Mitchell, Matthew Roughan

Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classific…

cs.NI2026

Robustness Analysis of Australia's Internet Using a Multilayer Network Model

Benjamin Lang, Matthew Roughan, Mengbin Ye

Australia depends on an Internet built from multiple networks of long-haul links. We study the interactions of these independent provider networks to investigate how the peering be…

cs.DS2025

A faster algorithm for efficient longest common substring calculation for non-parametric entropy estimation in sequential data

Bridget Smart, Max Ward, Matthew Roughan

Non-parametric entropy estimation on sequential data is a fundamental tool in signal processing, capturing information flow within or between processes to measure predictability, r…

q-fin.ST2025

Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)

Cameron Cornell, Lewis Mitchell, Matthew Roughan

Causal networks offer an intuitive framework to understand influence structures within time series systems. However, the presence of cycles can obscure dynamic relationships and hi…

math.PR2024

Elo Ratings in the Presence of Intransitivity

Adam H. Hamilton, Matthew Roughan, Anna Kalenkova

This paper studies how the Elo rating system behaves when the underlying modelling assumptions are not met.

q-fin.ST2024★ 1 cited

Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression

Cameron Cornell, Lewis Mitchell, Matthew Roughan

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, t…

cs.NE2024

Modified CMA-ES Algorithm for Multi-Modal Optimization: Incorporating Niching Strategies and Dynamic Adaptation Mechanism

Wathsala Karunarathne, Indu Bala, Dikshit Chauhan +2

This study modifies the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm for multi-modal optimization problems. The enhancements focus on addressing the challenge…