# Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search

arXiv: https://arxiv.org/abs/2006.11459 · HTML: https://mlsanity.com/paper/2006.11459/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series](https://mlsanity.com/paper/2207.12208/) (2022)
- [dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification](https://mlsanity.com/paper/2207.12165/) (2022)
- [Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints](https://mlsanity.com/paper/2203.13601/) (2022)
- [Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching](https://mlsanity.com/paper/2112.06671/) (2021)
- [An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]](https://mlsanity.com/paper/2603.23710/) (2026)
- [SEAnet: A Deep Learning Architecture for Data Series Similarity Search](https://mlsanity.com/paper/2603.01448/) (2026)

## Abstract (arXiv)

Data series are a special type of multidimensional data present in numerous domains, where similarity search is a key operation that has been extensively studied in the data series literature. In parallel, the multidimensional community has studied approximate similarity search techniques. We propose a taxonomy of similarity search techniques that reconciles the terminology used in these two domains, we describe modifications to data series indexing techniques enabling them to answer approximate similarity queries with quality guarantees, and we conduct a thorough experimental evaluation to compare approximate similarity search techniques under a unified framework, on synthetic and real datasets in memory and on disk. Although data series differ from generic multidimensional vectors (series usually exhibit correlation between neighboring values), our results show that data series techniques answer approximate %similarity queries with strong guarantees and an excellent empirical performance, on data series and vectors alike. These techniques outperform the state-of-the-art approximate techniques for vectors when operating on disk, and remain competitive in memory.