# QUANT: A Minimalist Interval Method for Time Series Classification

arXiv: https://arxiv.org/abs/2308.00928 · HTML: https://mlsanity.com/paper/2308.00928/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification](https://mlsanity.com/paper/2102.00457/) (2021)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [The Canonical Interval Forest (CIF) Classifier for Time Series Classification](https://mlsanity.com/paper/2008.09172/) (2020)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification](https://mlsanity.com/paper/2105.03841/) (2021)
- [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021)
- [The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier](https://mlsanity.com/paper/2201.12048/) (2022)
- [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021)
- [Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series](https://mlsanity.com/paper/2304.05800/) (2023)
- [Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification](https://mlsanity.com/paper/2303.17809/) (2023)

## Built on by (most cited first, 15 of 15)

- [MONSTER: Monash Scalable Time Series Evaluation Repository](https://mlsanity.com/paper/2502.15122/) (2025)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [Time series classification with random convolution kernels: pooling operators and input representations matter](https://mlsanity.com/paper/2409.01115/) (2024)
- [An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification](https://mlsanity.com/paper/2509.03649/) (2025)
- [Watch Your Step: A Cost-Sensitive Framework for Accelerometer-Based Fall Detection in Real-World Streaming Scenarios](https://mlsanity.com/paper/2509.11789/) (2025)
- [Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification](https://mlsanity.com/paper/2605.00218/) (2026)
- [In-Context Time Series Classification with Random Convolutional Features](https://mlsanity.com/paper/2607.19234/) (2026)
- [A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series](https://mlsanity.com/paper/2412.00772/) (2024)
- [Closed-Form Beta Distribution Estimation from Sparse Statistics with Random Forest Implicit Regularization](https://mlsanity.com/paper/2507.23767/) (2025)
- [Learning Interpretable Differentiable Logic Networks for Time-Series Classification](https://mlsanity.com/paper/2508.17512/) (2025)
- [Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification](https://mlsanity.com/paper/2604.07953/) (2026)
- [Univariate Channel Fusion for Multivariate Time Series Classification](https://mlsanity.com/paper/2604.16119/) (2026)
- [A Simple State Space Model Excels at Multivariate Time Series Classification](https://mlsanity.com/paper/2605.27406/) (2026)
- [Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity](https://mlsanity.com/paper/2608.13197/) (2026)
- [KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees](https://mlsanity.com/paper/2608.23393/) (2026)

## Abstract (arXiv)

We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 minutes using a single CPU core.