# A review on distance based time series classification

arXiv: https://arxiv.org/abs/1806.04509 · HTML: https://mlsanity.com/paper/1806.04509/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 30 of 40)

- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences](https://mlsanity.com/paper/2202.11485/) (2022)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [Towards Similarity-Aware Time-Series Classification](https://mlsanity.com/paper/2201.01413/) (2022)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification](https://mlsanity.com/paper/2410.18686/) (2024)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [Enhancing Transformer Efficiency for Multivariate Time Series Classification](https://mlsanity.com/paper/2203.14472/) (2022)
- [An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification](https://mlsanity.com/paper/2104.08153/) (2021)
- [LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning](https://mlsanity.com/paper/2207.12007/) (2022)
- [Fast Gradient Computation for Gromov-Wasserstein Distance](https://mlsanity.com/paper/2404.08970/) (2024)
- [Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering](https://mlsanity.com/paper/2411.17838/) (2024)
- [Forecasting the abnormal events at well drilling with machine learning](https://mlsanity.com/paper/2203.05378/) (2022)
- [A walk through of time series analysis on quantum computers](https://mlsanity.com/paper/2205.00986/) (2022)
- [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023)
- [Intelligent Mode-switching Framework for Teleoperation](https://mlsanity.com/paper/2402.06047/) (2024)
- [Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?](https://mlsanity.com/paper/2402.09082/) (2024)
- [LETS-C: Leveraging Text Embedding for Time Series Classification](https://mlsanity.com/paper/2407.06533/) (2024)
- [Adaptive Classification of Interval-Valued Time Series](https://mlsanity.com/paper/2504.03318/) (2025)
- [Measuring Time-Series Dataset Similarity using Wasserstein Distance](https://mlsanity.com/paper/2507.22189/) (2025)
- [Local regression on path spaces with signature metrics](https://mlsanity.com/paper/2510.16728/) (2025)
- [Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification](https://mlsanity.com/paper/2601.01150/) (2026)
- [Early Time-Series Classification Algorithms: An Empirical Comparison](https://mlsanity.com/paper/2203.01628/) (2022)
- [A Review of Open Source Software Tools for Time Series Analysis](https://mlsanity.com/paper/2203.05195/) (2022)
- [Classification ensembles for multivariate functional data with application to mouse movements in web surveys](https://mlsanity.com/paper/2205.13380/) (2022)
- [Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis](https://mlsanity.com/paper/2206.01984/) (2022)

## Abstract (arXiv)

Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to it simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.