Interactive visual analysis of causality in temporal data
As data storage technology is increasingly improving,the correlations of variables in time series data are more complex.It is difficult to artificially speculate on the causalities based on previous accumulated experience to sup-port the exploration of deeper relationships.The use of machine algorithms to detect the causality between multivari-ate time series data and exert the potential value of data has important practical significance for the application of big data in marketing and health care.Aiming at low efficiency issues,high error rate and low interpretability of causality models in time series data,this paper combines the functional greedy equivalence search(F-GES)model with the Granger causality model for causal inference,and proposes an interactive causality visual analysis ap-proach,which includes the parameter view to improve the efficiency of causality exploration,the causality tree to visually display the causalities,the time view to compare the original time series data,and the streamgraph view for users to explore the hierarchical evolution of raw dataset,and parallel coordinate to analyze correlations among vari-ables.This system supports interactive visual manipulation,verification,and summarization of causal relationships in time series data.Thus,mining causalities between variables in time series data can help users for decision-mak-ing.
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