An Online Change Point Detection Algorithm Based on Difference-in-Differences for Time Series
- DOI
- 10.2991/978-94-6463-992-6_4How to use a DOI?
- Keywords
- DID; online change point detection; AIC optimization; air quality
- Abstract
Change point detection is widely used in various fields, such as finance and environmental monitoring, to identify significant shifts in the underlying state of the process under study. This paper introduces the Difference-in-Differences(DID) Online Change Point Detection(DOCPD) algorithm, which combines the DID method with a recursive refinement and the Akaike Information Criterion(AIC) optimization strategy for online change point detection in time series data. The algorithm enhances detection accuracy and robustness, and quantifies change point strength. Finally, real-world air quality data is used for empirical analysis. The results show that DOCPD algorithm can not only detect change points in complex scenarios, but also quantify the intensity of change points, providing insights for pollution management.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Renjie Chu AU - Yunxia Li AU - Shijie Gao PY - 2026 DA - 2026/02/20 TI - An Online Change Point Detection Algorithm Based on Difference-in-Differences for Time Series BT - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025) PB - Atlantis Press SP - 22 EP - 28 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-992-6_4 DO - 10.2991/978-94-6463-992-6_4 ID - Chu2026 ER -