Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series
This work addresses the problem of effective visual analytics for time series data, which is incremental by building on prior network-based methods for time series characterization.
The paper tackles the challenge of visualizing and analyzing time series data by converting it into complex networks based on first-order Markov processes, resulting in more intuitive visualizations that preserve temporal dependencies and frequency structures, with experimental validation on tasks like pattern discovery and classification using synthetic and real data.
Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry especially in sensor and IoT related research and applications, but few advances has been achieved in effective time series visual analytics and interaction due to its temporal dimensionality and complex dynamics. Inspired by recent effort on using network metrics to characterize time series for classification, we present an approach to visualize time series as complex networks based on the first order Markov process in its temporal ordering. In contrast to the classical bar charts, line plots and other statistics based graph, our approach delivers more intuitive visualization that better preserves both the temporal dependency and frequency structures. It provides a natural inverse operation to map the graph back to raw signals, making it possible to use graph statistics to characterize time series for better visual exploration and statistical analysis. Our experimental results suggest the effectiveness on various tasks such as pattern discovery and classification on both synthetic and the real time series and sensor data.