摘要
We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes.A data structure representing a single episode is a multivariate time series.To analyse collections of episodes,we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes.Each episode is thus represented by a combination of patterns.Using this representation,we apply visual analytics techniques to fulfil a set of analysis tasks,such as investigation of the temporal distribution of the patterns,frequencies of transitions between the patterns in episode sequences,and co-occurrences of patterns of different variables within same episodes.We demonstrate our approach on two examples using real-world data,namely,dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover.
基金
supported by Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)
EU in projects SoBigData++and CrexData,and by DFG within priority research program SPP VGI(project EVA-VGI).
