Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis
It addresses data splitting issues for researchers in video and time series analysis, but is incremental as it reviews existing challenges without proposing new solutions.
This concept article examines challenges in splitting sequential data for video and time series analysis, such as data acquisition and split ratio selection, using examples from motor test benches and particle tracking.
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.