1 citations · 1 across the 13 of their papers we have counts for
16 papers
SteerCast: Retrieval-Based Latent Steering for Decoder-Only Time Series Forecasting
Van Dai Do, Huu Hiep Nguyen, Minh Hoang Nguyen +1
Time series forecasting aims to predict future values from historical observations and auxiliary features. We propose \textbf{SteerCast}, a retrieval-based latent steering method t…
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…
Expert-Guided Forecast Editing for Time-Series Foundation Models
Hung Le, Minh Hoang Nguyen, Manh Nguyen +2
Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate…
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…
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen +1
Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answeri…
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…
Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting
Hung Le, Sherif Abbas, Minh Hoang Nguyen +3
Efficient molecular dynamics (MD) simulation is vital for understanding atomic-scale processes in materials science and biophysics. Traditional density functional theory (DFT) meth…
Multi-Reference Preference Optimization for Large Language Models
Hung Le, Quan Tran, Dung Nguyen +4
How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingl…
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory
Hung Le, Dung Nguyen, Kien Do +2
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data. PANM integrates an external neura…
Revisiting the Dataset Bias Problem from a Statistical Perspective
Kien Do, Dung Nguyen, Hung Le +6
In this paper, we study the "dataset bias" problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u a…
Variational Flow Models: Flowing in Your Style
Kien Do, Duc Kieu, Toan Nguyen +4
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+σ_{t}X_{1} into a str…
SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records
Hung Le, Ong Eng-Jon, Bober Miroslaw
The accurate prediction of survival times for patients with severe diseases remains a critical challenge despite recent advances in artificial intelligence. This study introduces "…