6 papers
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…
Entropy Guided Dynamic Patch Segmentation for Time Series Transformers
Sachith Abeywickrama, Emadeldeen Eldele, Min Wu +2
Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic p…
UniFault: A Fault Diagnosis Foundation Model from Bearing Data
Emadeldeen Eldele, Mohamed Ragab, Xu Qing +5
Machine fault diagnosis (FD) is a critical task for predictive maintenance, enabling early fault detection and preventing unexpected failures. Despite its importance, existing FD m…
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
Yanru Sun, Emadeldeen Eldele, Zongxia Xie +5
Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks…
Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design
Omar A. M. Abdelraouf, Abdulrahman M. A. Ahmed, Emadeldeen Eldele +1
The fusion of artificial intelligence (AI) with physics-guided frameworks has opened transformative avenues for advancing the design and optimization of electromagnetic and nanopho…
Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization
Peiliang Gong, Emadeldeen Eldele, Min Wu +3
Foundation models have achieved remarkable success across diverse machine-learning domains through large-scale pretraining on large, diverse datasets. However, pretraining on such…