  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.
