# cleanTS: Automated (AutoML) Tool to Clean Univariate Time Series at Microscales

arXiv: https://arxiv.org/abs/2110.11815 · HTML: https://mlsanity.com/paper/2110.11815/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2021.

## Built on by (most cited first, 7 of 7)

- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection](https://mlsanity.com/paper/2205.11755/) (2022)
- [An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone](https://mlsanity.com/paper/2309.07992/) (2023)
- [Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods](https://mlsanity.com/paper/2403.08352/) (2024)
- [LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning](https://mlsanity.com/paper/2601.20375/) (2026)
- [Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services](https://mlsanity.com/paper/2307.10653/) (2023)
- [ImputeGAP: A Comprehensive Library for Time Series Imputation](https://mlsanity.com/paper/2503.15250/) (2025)

## Abstract (arXiv)

Data cleaning is one of the most important tasks in data analysis processes. One of the perennial challenges in data analytics is the detection and handling of non-valid data. Failing to do so can result in inaccurate analytics and unreliable decisions. The process of properly cleaning such data takes much time. Errors are prevalent in time series data. It is usually found that real world data is unclean and requires some pre-processing. The analysis of large amounts of data is difficult. This paper is intended to provide an easy to use and reliable system which automates the cleaning process of univariate time series data. Automating the process greatly reduces the time required. Visualizing a large amount of data at once is not very effective. To tackle this issue, an R package cleanTS is proposed. The proposed system provides a way to analyze data on different scales and resolutions. Also, it provides users with tools and a benchmark system for comparing various techniques used in data cleaning.