# A Survey on Time-Series Distance Measures

arXiv: https://arxiv.org/abs/2412.20574 · HTML: https://mlsanity.com/paper/2412.20574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 202 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 23 of 23)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://mlsanity.com/paper/2308.08241/) (2023)
- [Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)](https://mlsanity.com/paper/1405.5869/) (2014)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review](https://mlsanity.com/paper/1708.04321/) (2017)
- [Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection](https://mlsanity.com/paper/1804.07091/) (2018)
- [Random Warping Series: A Random Features Method for Time-Series Embedding](https://mlsanity.com/paper/1809.05259/) (2018)
- [The Social Dynamics of Language Change in Online Networks](https://mlsanity.com/paper/1609.02075/) (2016)
- [Elastic bands across the path: A new framework and methods to lower bound DTW](https://mlsanity.com/paper/1808.09617/) (2018)
- [Model-based clustering and segmentation of time series with changes in regime](https://mlsanity.com/paper/1312.6967/) (2013)
- [The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art](https://mlsanity.com/paper/2006.11454/) (2020)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
- [Tight lower bounds for Dynamic Time Warping](https://mlsanity.com/paper/2102.07076/) (2021)
- [Early Abandoning PrunedDTW and its application to similarity search](https://mlsanity.com/paper/2010.05371/) (2020)
- [Light Curve Classification with DistClassiPy: a new distance-based classifier](https://mlsanity.com/paper/2403.12120/) (2024)
- [Introduction to finite mixtures](https://mlsanity.com/paper/1705.01505/) (2017)

## Built on by (most cited first, 3 of 3)

- [Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs](https://mlsanity.com/paper/2505.22686/) (2025)
- [Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain](https://mlsanity.com/paper/2605.22563/) (2026)
- [READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis](https://mlsanity.com/paper/2609.32123/) (2026)

## Abstract (arXiv)

Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the effectiveness and efficiency of these distance measures. To provide a comprehensive view of this field, this work considers over 100 state-of-the-art distance measures, classified into 7 categories: lock-step measures, sliding measures, elastic measures, kernel measures, feature-based measures, model-based measures, and embedding measures. Beyond providing comprehensive mathematical frameworks, this work also delves into the distinctions and applications across these categories for both univariate and multivariate cases. By providing comprehensive collections and insights, this study paves the way for the future development of innovative time-series distance measures.