Learning in graphical models
Tractable learning of large Bayes net structures from sparse data
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate inference, structure learning and feature estimation in markov random fields
Approximate inference, structure learning and feature estimation in markov random fields
Semi-supervised learning with data calibration for long-term time series forecasting
Proceedings of the 14th 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
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improving predictions using aggregate information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping decomposition for causal graphical modeling
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving quality control by early prediction of manufacturing outcomes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Using coarse information for real valued prediction
Data Mining and Knowledge Discovery
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Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learning and predicting with "static" temporal graphs only. However, in many applications such as mechanical systems and biology systems, the temporal dependencies might change over time. In this paper, we develop a dynamic temporal graphical models based on hidden Markov model regression and lasso-type algorithms. Our method is able to integrate two usually separate tasks, i.e. inferring underlying states and learning temporal graphs, in one unified model. The output temporal graphs provide better understanding about complex systems, i.e. how their dependency graphs evolve over time, and achieve more accurate predictions. We examine our model on two synthetic datasets as well as a real application dataset for monitoring oil-production equipment to capture different stages of the system, and achieve promising results.