TopoLines: Topological Smoothing for Line Charts
This addresses the issue of peak retention in line chart smoothing for visual analytics tasks, but it is incremental as it builds on existing smoothing methods with a specific focus.
The authors tackled the problem of smoothing noisy line charts while preserving peaks, and presented TopoLines, a method using topological data analysis that maintains prominent peaks with minimized residual error, evaluated against 5 other methods across 4 domains.
Line charts are commonly used to visualize a series of data values. When the data are noisy, smoothing is applied to make the signal more apparent. Conventional methods used to smooth line charts, e.g., using subsampling or filters, such as median, Gaussian, or low-pass, each optimize for different properties of the data. The properties generally do not include retaining peaks (i.e., local minima and maxima) in the data, which is an important feature for certain visual analytics tasks. We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.