9 papers · 1 filter
SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
Tuan-Binh Tran, Dat Nguyen Cong, Duc-Trong Le +2
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not…
When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
Dat Nguyen-Cong, Luong Tran, Tung Kieu
Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement pro…
ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation
Xuan-Thong Truong, Trung-Kien Le, Tung Kieu +2
Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within the corrupted input sequence. T…
TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be obtained. Further, the emer…
Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version
Hong-Phuc Phan, Tuan-Anh Vu, Tung Kieu +3
Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve detection robustness. However, com…
Universal Multi-Domain Translation via Diffusion Routers
Duc Kieu, Kien Do, Tuan Hoang +4
Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen…