Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis

TL;DR

This paper discusses the challenges in splitting sequential data like videos and time series, highlighting issues in data acquisition, representation, and strategy selection through real-world examples.

Contribution

It provides a comprehensive analysis of the challenges in splitting sequential data and offers insights based on real-world case studies.

Findings

Identified key challenges in data splitting processes.

Analyzed impact of splitting choices on analysis accuracy.

Presented real-world examples illustrating these challenges.

Abstract

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.