Modern Spectrum Methods in Time Series Analysis: Physical Science, Environmental Science and Computer Modeling

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Collaborative Research Team Project: 2015-2018

Natural time series in geophysics and solar physics often have complex stochastic structures and spectra with “many lines” that are not generally captured by low-order parametric models. These “many line” problems were first discovered while searching for causes of problems in engineering systems associated with the active solar maximum around 1990, leading to advances in robust spectral estimation. The multitude of periodic components underlines the importance of modeling for both physical understanding and careful statistical characterization. This project will pursue methods to model processes with such features as nearly periodic components, nonlinear coupling, non-stationarity and non-Gaussian distributions to devise appropriate statistical tests for their frequency domain parameters.

Collaborating internationally with physicists, engineers, and modelers, including researchers in Natural Resources Canada and Health Canada, the team will develop methods for exploratory data analysis of time series using multi-taper and related methods. They envisage applications to models of seismic “noise” background, solar gravity modes, environmental solar effects, pollutants and meteorological phenomena. In addition to extracting information from natural time series, they will also develop spectral estimation methods for the output of multi-fidelity computer models.

The project is led by David J. Thomson of Queen’s University. Other primary investigators are Glen Takahara and Devon Lin of Queen’s, Keith Thompson of Dalhousie University, Jean-Pierre St-Maurice of the University of Saskatchewan and Frank Vernon of the University of California, San Diego. Collaborators are Wesley Burr of Health Canada, Alan D. Chave of Woods Hole Oceanographic Institution, Martin Connors of Athabasca University, Colin Farquharson of Memorial University of Newfoundland, Alexander (Sasha) Koustov of the University of Saskatchewan, Germán A. Prieto of Massachusetts Institute of Technology, Laureline Sangalli of Royal Military College, and Karin Sigloch of Oxford.

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