Time Series Analysis with the Causality Workbench

Isabelle Guyon, Alexander Satnikov, Constantin Aliferis
Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, PMLR 12:115-139, 2011.

Abstract

The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal http://clopinet.com/causality, it provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we describe what the platform offers for the analysis of causality in time series analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-v12-guyon11, title = {Time Series Analysis with the Causality Workbench}, author = {Guyon, Isabelle and Satnikov, Alexander and Aliferis, Constantin}, booktitle = {Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series}, pages = {115--139}, year = {2011}, editor = {Popescu, Florin and Guyon, Isabelle}, volume = {12}, series = {Proceedings of Machine Learning Research}, address = {Vancouver, Canada}, month = {10 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v12/guyon11/guyon11.pdf}, url = {https://proceedings.mlr.press/v12/guyon11.html}, abstract = {The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal http://clopinet.com/causality, it provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we describe what the platform offers for the analysis of causality in time series analysis.} }
Endnote
%0 Conference Paper %T Time Series Analysis with the Causality Workbench %A Isabelle Guyon %A Alexander Satnikov %A Constantin Aliferis %B Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series %C Proceedings of Machine Learning Research %D 2011 %E Florin Popescu %E Isabelle Guyon %F pmlr-v12-guyon11 %I PMLR %P 115--139 %U https://proceedings.mlr.press/v12/guyon11.html %V 12 %X The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal http://clopinet.com/causality, it provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we describe what the platform offers for the analysis of causality in time series analysis.
RIS
TY - CPAPER TI - Time Series Analysis with the Causality Workbench AU - Isabelle Guyon AU - Alexander Satnikov AU - Constantin Aliferis BT - Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series DA - 2011/03/03 ED - Florin Popescu ED - Isabelle Guyon ID - pmlr-v12-guyon11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 12 SP - 115 EP - 139 L1 - http://proceedings.mlr.press/v12/guyon11/guyon11.pdf UR - https://proceedings.mlr.press/v12/guyon11.html AB - The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal http://clopinet.com/causality, it provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we describe what the platform offers for the analysis of causality in time series analysis. ER -
APA
Guyon, I., Satnikov, A. & Aliferis, C.. (2011). Time Series Analysis with the Causality Workbench. Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, in Proceedings of Machine Learning Research 12:115-139 Available from https://proceedings.mlr.press/v12/guyon11.html.

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