Composite Visual Mapping for Time Series Visualization
121 pages. 2018.
Time series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets’ volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.
Visualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives. Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.
Next, we present our contributions. Our first contribution is a systematic study of a composite visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users’ speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constrained situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization.