Identifying, exploring, and interpreting time series shapes in multivariate time intervals

We introduce a concept of episode referring to time interval in the development dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing single multivariate series. To analyse collections episodes, we propose an approach based on recognition particular patterns temporal variation variables within episodes. Each thus represented combination patterns. Using this representation, apply visual analytics techniques fulfil set analysis tasks, such as investigation distribution patterns, frequencies transitions between sequences, and co-occurrences different same demonstrate our two examples using real-world data, namely, dynamics human mobility indicators during COVID-19 pandemic characteristics football team movements episodes ball turnover.