Time Series Information Visualization -- A Review of Approaches and Tools

TL;DR

This paper reviews various techniques and tools for visualizing time series data, emphasizing their role in understanding complex, multi-feature temporal datasets and guiding future research in the field.

Contribution

It provides a comprehensive overview of existing visualization approaches, theoretical insights, and design guidelines for time series data with multiple features.

Findings

Highlights challenges in visualizing multi-feature time series

Provides design guidelines for effective visualization systems

Identifies future research directions in the field

Abstract

Time series data are prevalent across various domains and often encompass large datasets containing multiple time-dependent features in each sample. Exploring time-varying data is critical for data science practitioners aiming to understand dynamic behaviors and discover periodic patterns and trends. However, the analysis of such data often requires sophisticated procedures and tools. Information visualization is a communication channel that leverages human perceptual abilities to transform abstract data into visual representations. Visualization techniques have been successfully applied in the context of time series to enhance interpretability by graphically representing the temporal evolution of data. The challenge for information visualization developers lies in integrating a wide range of analytical tools into rich visualization systems that can summarize complex datasets while clearly describing the impacts of the temporal component. Such systems enable data scientists to turn raw data into understandable and potentially useful knowledge. This review examines techniques and approaches designed for handling time series data, guiding users through knowledge discovery processes based on visual analysis. We also provide readers with theoretical insights and design guidelines for considering when developing comprehensive information visualization approaches for time series, with a particular focus on time series with multiple features. As a result, we highlight the challenges and future research directions to address open questions in the visualization of time-dependent data.