Time series visualization based on shape features

Time series visualization is one of the most fundamental tasks, which is often used to discover patterns by a user interface. Many increasing interests in time series visualization in the last decade have resulted in the development of various state-of-the-art visualization techniques. In most cases, time series visualization is based on one of the representation frameworks which not only reduce the dimensionality, but sufficiently reflect the shapes of the raw time series as well. In this paper, a new time series visualization based on shape features is proposed to discover surprising patterns and mine frequent trends (motifs discovery). Since the shape features validly summarize both the global and local structures of time series and often use the slopes (or the angles) of the changeable values over time to describe the trends of time series, part of the work is to extract the important shape features and transform them into symbol string. After dimensionality reduction and symbol representation, a circle plotted by the visualization technique is split into many small sectors which simultaneously represent substring patterns of unequal length by multi-resolution function. Since the overall shape features of time series are based on data point importance, the surprising patterns and frequent trends can be accurately discovered even under a high compress ratio. The refined results of patterns discovery and frequent trends mining on financial and other time series datasets indicate that the proposed approach is an effective visualization tool for time series mining.