@inproceedings{2b6740bee2bb4b39b8c46e8d461af8aa,
title = "Finding dynamic co-evolving zones in spatial-temporal time series data",
abstract = "Co-evolving patterns exist in many Spatial-temporal time series Data, which shows invaluable information about evolving patterns of the data. However, due to the sensor readings{\textquoteright} spatial and temporal heterogeneity, how to find the stable and dynamic co-evolving zones remains an unsolved issue. In this paper, we proposed a novel divide-and-conquer strategy to find the dynamic co-evolving zones that systematically leverages the heterogeneity challenges. The precision of spatial inference and temporal prediction improved by 7\% and 8\% respectively by using the found patterns, which shows the effectiveness of the found patterns. The system has also been deployed with the Haidian Ministry of Environmental Protection, Beijing, China, providing accurate spatial-temporal predictions and help the government make more scientific strategies for environment treatment.",
keywords = "Air quality, Co-evolving, Time series clustering",
author = "Yun Cheng and Xiucheng Li and Yan Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",
year = "2016",
doi = "10.1007/978-3-319-46131-1\_20",
language = "英语",
isbn = "9783319461304",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "129--144",
editor = "Bettina Berendt and Bj{\"o}rn Bringmann and Elisa Fromont and Gemma Garriga and Pauli Miettinen and Nikolaj Tatti and Volker Tresp",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings",
address = "德国",
}