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’ 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.