# SAX Navigator: Time Series Exploration through Hierarchical Clustering

arXiv: https://arxiv.org/abs/1908.05505 · HTML: https://mlsanity.com/paper/1908.05505/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [Data+Shift: Supporting visual investigation of data distribution shifts by data scientists](https://mlsanity.com/paper/2204.14025/) (2022)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback](https://mlsanity.com/paper/2104.14962/) (2021)
- [SigTime: Learning and Visually Explaining Time Series Signatures](https://mlsanity.com/paper/2512.12076/) (2025)
- [QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation](https://mlsanity.com/paper/2411.15209/) (2024)
- [TiVy: Time Series Visual Summary for Scalable Visualization](https://mlsanity.com/paper/2507.18972/) (2025)

## Abstract (arXiv)

Comparing many long time series is challenging to do by hand. Clustering time series enables data analysts to discover relevance between and anomalies among multiple time series. However, even after reasonable clustering, analysts have to scrutinize correlations between clusters or similarities within a cluster. We developed SAX Navigator, an interactive visualization tool, that allows users to hierarchically explore global patterns as well as individual observations across large collections of time series data. Our visualization provides a unique way to navigate time series that involves a "vocabulary of patterns" developed by using a dimensionality reduction technique,Symbolic Aggregate approXimation(SAX). With SAX, the time series data clusters efficiently and is quicker to query at scale. We demonstrate the ability of SAX Navigator to analyze patterns in large time series data based on three case studies for an astronomy data set. We verify the usability of our system through a think-aloud study with an astronomy domain scientist.