4 papers
Beyond Gaussian Assumptions: A Nonlinear Generalization of Linear Inverse Modeling
Justin Lien, Hiroyasu Ando
The Linear Inverse Model (LIM) is a class of data-driven methods that construct approximate linear stochastic models to represent complex observational data. The stochastic forcing…
On the Cyclostationary Linear Inverse Models: A Mathematical Insight and Implication
Justin Lien, Yan-Ning Kuo, Hiroyasu Ando
Cyclostationary linear inverse models (CS-LIMs), generalized versions of the classical (stationary) LIM, are advanced data-driven techniques for extracting the first-order time-dep…
Colored-LIM: A Data-Driven Method for Studying Dynamical Systems with Temporally Correlated Stochasticity
Justin Lien, Yan-Ning Kuo, Hiroyasu Ando +1
In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The Linear Inverse Model (LIM) is a class of data-d…
Hypergraph Echo State Network
Justin Lien
A hypergraph as a generalization of graphs records higher-order interactions among nodes, yields a more flexible network model, and allows non-linear features for a group of nodes.…