Spatio-Temporal Change Detection Using Granger Sequence Pattern
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5202-5203. https://doi.org/10.24963/ijcai.2020/741
This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.
Data Mining: Mining Spatial, Temporal Data
Data Mining: Frequent Pattern Mining
Machine Learning: Time-series;Data Streams
Machine Learning: Clustering