Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
A tutorial on learning with Bayesian networks
Learning in graphical models
A unifying review of linear Gaussian models
Neural Computation
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
Finding a causal ordering via independent component analysis
Computational Statistics & Data Analysis
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Detection of unique temporal segments by information theoretic meta-clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Grouped graphical Granger modeling methods for temporal causal modeling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning dynamic temporal graphs for oil-production equipment monitoring system
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards automated performance diagnosis in a large IPTV network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
Towards proactive event-driven computing
Proceedings of the 5th ACM international conference on Distributed event-based system
Improving predictions using aggregate information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Information transfer in social media
Proceedings of the 21st international conference on World Wide Web
Overlapping decomposition for causal graphical modeling
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
Towards Twitter context summarization with user influence models
Proceedings of the sixth ACM international conference on Web search and data mining
Using coarse information for real valued prediction
Data Mining and Knowledge Discovery
GCBN: a hybrid spatio-temporal causal model for traffic analysis and prediction
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data, which raises the challenge of how best to leverage the temporal information for causal modeling. Recently graphical modeling with the concept of "Granger causality", based on the intuition that a cause helps predict its effects in the future, has gained attention in many domains involving time series data analysis. With the surge of interest in model selection methodologies for regression, such as the Lasso, as practical alternatives to solving structural learning of graphical models, the question arises whether and how to combine these two notions into a practically viable approach for temporal causal modeling. In this paper, we examine a host of related algorithms that, loosely speaking, fall under the category of graphical Granger methods, and characterize their relative performance from multiple viewpoints. Our experiments show, for instance, that the Lasso algorithm exhibits consistent gain over the canonical pairwise graphical Granger method. We also characterize conditions under which these variants of graphical Granger methods perform well in comparison to other benchmark methods. Finally, we apply these methods to a real world data set involving key performance indicators of corporations, and present some concrete results.