# Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing (Technical Report)

arXiv: https://arxiv.org/abs/2003.12396 · HTML: https://mlsanity.com/paper/2003.12396/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

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

- [Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version](https://mlsanity.com/paper/2111.11108/) (2021)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [TOD: GPU-accelerated Outlier Detection via Tensor Operations](https://mlsanity.com/paper/2110.14007/) (2021)
- [Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning](https://mlsanity.com/paper/2109.06562/) (2021)
- [Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning](https://mlsanity.com/paper/2304.04275/) (2023)
- [AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties](https://mlsanity.com/paper/2410.24178/) (2024)
- [Multistage Large Segment Imputation Framework Based on Deep Learning and Statistic Metrics](https://mlsanity.com/paper/2209.11766/) (2022)

## Abstract (arXiv)

Errors are prevalent in time series data, such as GPS trajectories or sensor readings. Existing methods focus more on anomaly detection but not on repairing the detected anomalies. By simply filtering out the dirty data via anomaly detection, applications could still be unreliable over the incomplete time series. Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing. Our major contributions include: (1) a novel framework of iterative minimum repairing (IMR) over time series data, (2) explicit analysis on convergence of the proposed iterative minimum repairing, and (3) efficient estimation of parameters in each iteration. Remarkably, with incremental computation, we reduce the complexity of parameter estimation from O(n) to O(1). Experiments on real datasets demonstrate the superiority of our proposal compared to the state-of-the-art approaches. In particular, we show that (the proposed) repairing indeed improves the time series classification application.