Granger Causal Network Learning and the Depth Wise Grouped LASSO

Time Series;Granger Causality;Sparsity;Grouped LASSO;Convex Optimization

Kinnear, Ryanadvisor:Mazumdar, Ravi ; affiliation1:Faculty of Engineering ; Mazumdar, Ravi ;

University of Waterloo

关键词: Grouped LASSO;    Master Thesis;    Sparsity;    Time Series;    Convex Optimization;    Granger Causality;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/12316/6/kinnear_ryan.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

In this thesis we study the notion of Granger-causality, a statistical concept originally developed to estimate causal effects in econometrics. First, we suggest a more general notion of Granger-causality in which to frame the proceeding practical developments.And second, we derive a proximal optimization algorithm to fit large and sparse vector autoregressive models, a task closely connected to the estimation Granger-causality amongst jointly wide sense stationary process. Experimental results from our so called ;;Depth Wise Grouped LASSO” convex program are obtained for both simulated data, as well as Canadian meteorology data. We conclude by discussing some applications and by suggesting future research questions.