Multi-Granular Trend Detection for Time-Series Analysis

Goethem Arthur Van, Frank Staals, Maarten Loffler, Jason Dykes, Bettina Speckmann
2017 IEEE Transactions on Visualization and Computer Graphics  
This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: http://openaccess.city.ac.uk/15761/ Link to published version: http://dx.Fig. 1. Left: automated trend detection in combination with manual interaction finds two opposing key trends in the data (CO2 intensity of economic output: kg CO2 per 2005 PPP $ of GDP [13]). Middle: selecting only the subset that is part of the peak at T 1, directly shows that
more » ... his subset is not part of the peak at T 2 (homogenized daily amount of precipitation for 240 weather stations [21]). Right: different visual styles help (de-)emphasize different properties of the detected trends. Abstract-Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in onedimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.
doi:10.1109/tvcg.2016.2598619 pmid:27875181 fatcat:g5pyyz2b4bda5eltbwzusl75ki