# Supervised Time Series Classification for Anomaly Detection in Subsea Engineering

arXiv: https://arxiv.org/abs/2403.08013 · HTML: https://mlsanity.com/paper/2403.08013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 12 Mar 2024.

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

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)

## Built on by (most cited first, 1 of 1)

- [Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2510.01970/) (2025)

## Abstract (arXiv)

Time series classification is of significant importance in monitoring structural systems. In this work, we investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocessing of temporal data, using measures of statistical dispersion and dimension reduction techniques. We present an intuitive baseline method and discuss its efficiency. We conclude with a comparison of the various methods based on different performance metrics, showing the advantage of using machine learning techniques as a tool in decision making.