Due to the ubiquitous collection of data over time, important events like natural disasters, financial crises, or mass displacements of people often leave traces in environmental, economic, or other time series. Anomalies, extreme values, and significant changes in these time series can indicate the occurrence of such events. Hence, a lot of research focuses on detecting unusual patterns in time series to improve our understanding of the underlying systems and enable early warning systems. However, there is little agreement on how to precisely quantify the effect of discrete events on continuous time series in an interpretable way. In this project, we aim at filling this gap by exploring novel methods to correlate time series with event series.
