Unraveling the Complexity of Splitting Sequential Data: Tackling
  Challenges in Video and Time Series Analysis
Diego Botache Kristina Dingel Rico Huhnstock Arno Ehresmann Bernhard Sick
Splitting of sequential data, such as videos and time series, is an essential
step in various data analysis tasks, including object tracking and anomaly
detection. However, splitting sequential data presents a variety of challenges
that can impact the accuracy and reliability of subsequent analyses. This
concept article examines the challenges associated with splitting sequential
data, including data acquisition, data representation, split ratio selection,
setting up quality criteria, and choosing suitable selection strategies. We
explore these challenges through two real-world examples: motor test benches
and particle tracking in liquids.