Uncovering Temporal Patterns in Visualizations of High-Dimensional Data

TL;DR

This paper introduces a method to enhance dimensionality reduction techniques by explicitly incorporating temporal information, enabling better visualization of temporal patterns in high-dimensional, time-series data.

Contribution

It proposes a formal extension to existing embedding methods with temporal loss terms, improving temporal coherence and interpretability of visualizations.

Findings

Effectively uncovers temporal patterns in visualizations.

Improves temporal coherence without sacrificing embedding fidelity.

Demonstrated on synthetic and real-world datasets.

Abstract

With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic data analysis.