9 papers · 1 filter
Volatility-Clustering Adaptation for Financial Time Series
Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen +2
Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more target data yields better forecast…
Memory in Deep Time-Series Models
Minh Hoang Nguyen, Huu Hiep Nguyen, Manh Nguyen +3
Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state-space models, retrieval-augmen…
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen +2
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints th…
ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models
Kejing Wang, Toan Nguyen, Minh Hoang Nguyen +2
Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional autoregressive action polici…
Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts
Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen +1
Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training. A common…
Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Multimodal time series forecasting, which pairs numerical sequences with domain-relevant textual reports, promises to inject world knowledge into forecasting pipelines. However, we…
Spectral Retrieval-Augmented Time-Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are…
Reviving Error Correction in Modern Deep Time-Series Forecasting
Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3
Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autore…
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively…