Air quality visualization analysis based on multivariate time series data feature extraction

Air quality analysis helps analysts understand the state of atmospheric pollution and its changing trends, providing robust data and theoretical support for developing and implementing environmental policies. Air quality data are typically represented as multivariate time series, which poses challenges due to the large amount of data, high dimensionality, and lack of labeled information. Analysts often struggle to discover internal relationships and patterns within the data. There is still significant room for improvement in related data mining and exploration methods, as issues such as perceptual burden and low efficiency must be addressed. To assist analysts in atmospheric pollution analysis, we propose an air quality visualization scheme based on feature extraction of multivariate time series data. We utilize the automated data modeling capability of deep learning and intuitive data visualization to help analysts explore and analyze complex air quality datasets. To extract features of air quality data effectively, we transform the multivariate time series feature extraction task into an automated deep learning self-supervised task and propose a feature extraction method called CTDCN for multivariate time series. Finally, we design and implement a visualization and analysis system for air quality multivariate time series. This system helps analysts discover potential information and patterns in air quality data, providing support and a foundation for informed decision-making. The system offers rich visualization views, allows users to change data modeling parameters, and interactively analyze and extract insights from the data through multiple views. Extensive experiments on UEA public datasets confirm CTDCN’s superior feature extraction capabilities, while case studies and user studies validate the effectiveness and practicality of our visualization approach. Graphical abstract