# An Experimental Evaluation of Nearest Neighbour Time Series Classification

arXiv: https://arxiv.org/abs/1406.4757 · HTML: https://mlsanity.com/paper/1406.4757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2014.

## Built on by (most cited first, 4 of 4)

- [HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKET](https://mlsanity.com/paper/2511.01572/) (2025)
- [Classification of Anomalies in Telecommunication Network KPI Time Series](https://mlsanity.com/paper/2308.16279/) (2023)
- [Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis](https://mlsanity.com/paper/2502.17061/) (2025)
- [The Effect of Missingness-Pattern Mismatch on Method Selection for Time-Series Classification: A Controlled Empirical Study](https://mlsanity.com/paper/2610.05368/) (2026)

## Abstract (arXiv)

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~citelines14elastic, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting k for k-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation.