Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in th...
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...
Change impact analysis aims at identifying software artifacts being affected by a change. In the past, this problem has been addressed by approaches relying on static, dynamic, a...
Michele Ceccarelli, Luigi Cerulo, Gerardo Canfora,...