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Granger causality and its learning algorithms have been widely used in many disciplines to study cause-effect relationship among time series variables. In this paper, we address computing challenges of state-of-art Granger causality learning algorithms, specially when facing increasing dimensionality of available datasets. We study how to leverage gradient boosting meta machine learning techniques to achieve accurate causality discovery and big data parallel techniques for efficient causality discovery from large temporal datasets. We propose two main algorithms for gradient boosting based causality learning, and parallel gradient boosting based causality learning. Our experiments show our proposed algorithms can achieve efficient learning in distributed environments with good learning accuracy.
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Data-Driven Performance Monitoring of Dynamical Systems Using Granger Causal Graphical Models
Abstract Data-driven analysis and monitoring of complex dynamical systems have been gaining popularity due to various reasons like ubiquitous sensing and advanced computation capabilities. A key rationale is that such systems inherently have high dimensionality and feature complex subsystem interactions due to which majority of the first-principle based methods become insufficient. We explore the family of a recently proposed probabilistic graphical modeling technique, called spatiotemporal pattern network (STPN) in order to capture the Granger causal relationships among observations in a dynamical system. We also show that this technique can be used for anomaly detection and root-cause analysis for real-life dynamical systems. In this context, we introduce the notion of Granger-STPN (G-STPN) inspired by the notion of Granger causality and introduce a new nonparametric technique to detect causality among dynamical systems observations. We experimentally validate our framework for detecting anomalies and analyzing root causes in a robotic arm platform and obtain superior results compared to when other causality metrics were used in previous frameworks.
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- Award ID(s):
- 1845969
- PAR ID:
- 10491956
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Dynamic Systems, Measurement, and Control
- Volume:
- 142
- Issue:
- 8
- ISSN:
- 0022-0434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4046673
