Causal discovery from time-series data has been a central task in machine learning.
Recently, Granger causality inference is gaining momentum due to its good explainability …

Causal discovery in time-series is a fundamental problem in the machine learning
community, enabling causal reasoning and decision-making in complex scenarios …

Time-series causal discovery (TSCD) is a fundamental problem of machine learning.
However, existing synthetic datasets cannot properly evaluate or predict the algorithms' …

Discovering causal structures of temporal processes is a major tool of scientific inquiry
because it helps us better understand and explain the mechanisms driving a phenomenon …

From ancient philosophers to modern economists, biologists, and other researchers, there
has been a continuous effort to unveil causal relations. The most formidable challenge lies …

Abstract Revisiting the Grassberger–Procaccia algorithm inspires us to introduce an
extremely simple estimator of the correlation dimension. For the new estimator, we also …

It is becoming increasingly clear that causal analysis of dynamical systems requires different
approaches than, for example, causal analysis of interconnected autoregressive processes …

Our proposed method for exploring the causal discovery of stochastic dynamic systems is
designed to overcome the limitations of existing methods in detecting hidden and common …

Data dimensionality informs us about data complexity and sets limit on the structure of
successful signal processing pipelines. In this work we revisit and improve the manifold …

Convergent cross mapping (CCM) is designed for causal discovery between coupled time
series for which Granger's method for detecting causality is shown to be unreliable. The …