1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
Ali Forootani, Raffaele Iervolino
This paper proposes a unified family of learnable Koopman operator parameterizations that integrate linear dynamical systems theory with modern deep learning forecasting architectu…
DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
Ali Forootani, Mohammad Khosravi, Masoud Barati
Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Tran…
Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, traditional FL suffers from communication overhead…
Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
Ali Forootani, Mohammad Khosravi
Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty. Although Tran…
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data…