7 citations · 17 across the 7 of their papers we have counts for
7 papers
Modality-aware Transformer for Financial Time series Forecasting
Hajar Emami, Xuan-Hong Dang, Yousaf Shah +1
Time series forecasting presents a significant challenge, particularly when its accuracy relies on external data sources rather than solely on historical values. This issue is prev…
Maximizing Information in Domain-Invariant Representation Improves Transfer Learning
Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang +3
We propose MaxDIRep, a domain adaptation method that improves the decomposition of data representations into domain-independent and domain-dependent components. Existing methods, s…
AutoAI-TS: AutoAI for Time Series Forecasting
Syed Yousaf Shah, Dhaval Patel, Long Vu +10
A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literat…
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, in many real-world applications, such as commercial cloud managem…
Outlier Detection from Network Data with Subnetwork Interpretation
Xuan-Hong Dang, Arlei Silva, Ambuj Singh +2
Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computi…
Graph Wavelets via Sparse Cuts: Extended Version
Arlei Silva, Xuan-Hong Dang, Prithwish Basu +2
Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Pro…
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data
Xuan Hong Dang, Ambuj K. Singh, Petko Bogdanov +2
Data mining practitioners are facing challenges from data with network structure. In this paper, we address a specific class of global-state networks which comprises of a set of ne…