Robust Autocorrelation for Period Detection in Time Series

Zhi Yang, Likun Hou, Xing Zhao

2025

Abstract

Autocorrelation is a key tool in time series period detection, but its sensitivity to outliers is a significant limitation. This paper introduces a robust autocorrelation method for period detection that minimizes the influence of outliers. By incorporating a moving average and applying a Median Absolute Deviation (MAD) filter to each cycle-subseries, we significantly enhance the robustness of the autocorrelation to outliers. The MAD filter identifies and corrects outliers in the cycle-subseries, based on the assumption that the cycle-subseries consists of a constant plus Gaussian noise. This innovative robust autocorrelation can effectively replace traditional autocorrelation in existing period detection algorithms. Additionally, we propose a new algorithm that leverages our robust autocorrelation. Both theoretical analysis and empirical tests on real-world and synthetic datasets indicate that period detection algorithms using our proposed robust autocorrelation outperform those using traditional autocorrelation. Furthermore, our proposed algorithm surpasses all other existing algorithms in comparison.

Paper Citation

in Harvard Style

Yang Z., Hou L. and Zhao X. (2025). Robust Autocorrelation for Period Detection in Time Series. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 36-44. DOI: 10.5220/0013092500003890

in Bibtex Style

@conference{icaart25,
author={Zhi Yang and Likun Hou and Xing Zhao},
title={Robust Autocorrelation for Period Detection in Time Series},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={36-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013092500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Robust Autocorrelation for Period Detection in Time Series
SN - 978-989-758-737-5
AU - Yang Z.
AU - Hou L.
AU - Zhao X.
PY - 2025
SP - 36
EP - 44
DO - 10.5220/0013092500003890
PB - SciTePress