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
