5 papers
PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
Amal Saadallah, Julia Tjus, Petra Wiederkeher +1
Accurate and robust time series forecasting is essential in many applications involving physical processes, such as manufacturing monitoring and astrophysical event detection. In t…
Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting
Amal Saadallah, Abdulaziz Al-Ademi
Time series forecasting poses significant challenges in non-stationary environments where underlying patterns evolve over time. In this work, we propose a novel framework that enha…
SHAP-Guided Regularization in Machine Learning Models
Amal Saadallah
Feature attribution methods such as SHapley Additive exPlanations (SHAP) have become instrumental in understanding machine learning models, but their role in guiding model optimiza…
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting
Matthias Jakobs, Amal Saadallah
Tree-based models have been successfully applied to a wide variety of tasks, including time series forecasting. They are increasingly in demand and widely accepted because of their…
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
Raphael Fischer, Amal Saadallah
Automated machine learning (AutoML) streamlines the creation of ML models. While most methods select the "best" model based on predictive quality, it's crucial to acknowledge other…