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

arXiv: https://arxiv.org/abs/2307.14294 · HTML: https://mlsanity.com/paper/2307.14294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Toward Optimal Probabilistic Active Learning Using a Bayesian Approach](https://mlsanity.com/paper/2006.01732/) (2020)

## Abstract (arXiv)

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.