2 citations · 5 across the 5 of their papers we have counts for
7 papers
Complexity-based Financial Stress Evaluation
Hongjian Xiao, Yao Lei Xu, Danilo P. Mandic
Financial markets typically exhibit dynamically complex properties as they undergo continuous interactions with economic and environmental factors. The Efficient Market Hypothesis…
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Yuyang Miao, Yao Xu, Danilo Mandic
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in…
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge…
Reducing Computational Complexity of Tensor Contractions via Tensor-Train Networks
Ilya Kisil, Giuseppe G. Calvi, Kriton Konstantinidis +2
There is a significant expansion in both volume and range of applications along with the concomitant increase in the variety of data sources. These ever-expanding trends have highl…
Graph Theory for Metro Traffic Modelling
Bruno Scalzo Dees, Yao Lei Xu, Anthony G. Constantinides +1
A unifying graph theoretic framework for the modelling of metro transportation networks is proposed. This is achieved by first introducing a basic graph framework for the modelling…
Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting
Yao Lei Xu, Giuseppe G. Calvi, Danilo P. Mandic
Recurrent Neural Networks (RNNs) represent the de facto standard machine learning tool for sequence modelling, owing to their expressive power and memory. However, when dealing wit…