A Visualization and Level-of-Detail Control Technique for Large Scale Time Series Data

We have various interesting time series data in our daily life, such as weather data (e.g., temperature and air pressure)and stock prices. Polyline chart is one of the most common ways to represent such time series data. We often draw multiple polylines in one space to compare the time variation of multiple values. However, it is often difficult to read the values if the number of polylines gets larger. This paper presents a technique for visualization and level-of-detail control of large number of time series data. The technique generates clusters of time series values,and selects representative values for each cluster, as a preprocessing. The technique then draws the representative values as polylines. It also provides a user interface so that users can interactively select interesting representatives,and explore the time series values which belong to the clusters of the representatives.