Granger Graphical Models 时间序列异常检测

Granger Graphical Models

USC Melady Lab Code Repository  University of Southern California

http://www-bcf.usc.edu/~liu32/SMAD.htm

"D:\搜狗高速下载\【KDD 2015】\Social Media Anomaly Detection Challenges and Solutions yan liu KDD2015_tutorial.pdf"

Lasso Granger

Lasso-Granger is an efficient algorithm for learning the temporal dependency among multiple time series based on variable selection using Lasso.

Reference:A. Arnold, Y. Liu, and N. Abe.Temporal causal modeling with graphical granger methods. In KDD, 2007.

Code:lassoGranger.m

Copula-Granger

Copula-Granger extends the power of Lasso-Granger to non-linear datasets. It uses the copula technique to separate the marginal properties of the joint distribution from its dependency structure.

Paper: Y. Liu, M. T. Bahadori, and H. Li, "Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Series Modeling", ICML 2012.

Code:copulaGranger.m

Granger Causality for Irregular Time Series

TheGeneralized Lasso Grangeris designed to discover the Granger causality relationship amongirregular time series; times series whose samples are not recorded on regularly spaced timestamps.

Reference: M. T. Bahadori and Yan Liu, "Granger Causality Analysis in Irregular Time Series", SDM 2012.

Code:iLasso.m

Granger Graphical Models for Anomaly Detection in Multivariate Time Series

Extensions of Granger graphical models are developed to detect anomalies in temporal dependence in multivariate time series data.

Reference: H. Qiu, Y. Liu,  N. Subrahmanya, W. Li.Granger Graphical Models for Time-Series Anomaly Detection. In International conference on Data Mining  (ICDM' 2012), 2012.

Code:GrangerAD.rar