Causal Discovery of Time Series

Dear Colleagues,

From ancient philosophers to modern scientists across many different fields, including mathematicians, physicists, economists, biologists, neuroscientists, and Earth scientists, we are engaged in a most fundamental issue of revealing causal relationships and interactions. Consider that causal inference must decide if the relationship is unidirectional or bidirectional, or only apparent—implied only by a co-founder. Learning functionality demands methods to distinguish causal relationships of the system’s components. While some definitions of causal relationships are stated in terms of interventions and observations of response, experimental design and algorithmic inference follow. Other respected notions are free of explicit interventions, including the Nobel prize work of Granger, in terms of observations of a free running process to associate information flow by discovery of variables that confidently but minimally allow future forecasts. The latter is common in the natural sciences, for example, where experimental design generally cannot conceive of interventions. Recently, causality research has exploded thanks to the big data afforded by abundant sensors, advancements of algorithmic and computational methods including machine learning, and the ever-increasing computational capacity of modern hardware. We are witnessing an era of enormous theoretical advances, methodological innovations, and ever more exciting applications from literally all scientific fields.

Prof. András Telcs
Prof. Erik M. Bollt
Dr. Zoltan Somogyvari
Guest Editors

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• causal discovery
• transfer entropy
• dynamic systems
• time delay embedding