Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

TL;DR

This paper introduces two innovative visualization designs, the order of magnitude horizon graph and the order of magnitude line chart, to effectively visualize large value ranges in time-series data, improving analysis tasks.

Contribution

The paper presents two novel visualization methods that explicitly split mantissa and exponent to handle large value ranges in time-series data, extending existing visualization techniques.

Findings

Order of magnitude horizon graph outperforms or matches existing methods in identification, discrimination, and estimation tasks.

Traditional horizon graphs perform better in trend detection tasks.

The designs are domain-independent and applicable to various large value range datasets.

Abstract

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.