# Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification

arXiv: https://arxiv.org/abs/2509.22919 · HTML: https://mlsanity.com/paper/2509.22919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [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)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification](https://mlsanity.com/paper/2102.00457/) (2021)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [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)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier](https://mlsanity.com/paper/2201.12048/) (2022)
- [RED CoMETS: An ensemble classifier for symbolically represented multivariate time series](https://mlsanity.com/paper/2307.13679/) (2023)
- [Forest Proximities for Time Series](https://mlsanity.com/paper/2410.03098/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Generalized Proximity Forest](https://mlsanity.com/paper/2511.19487/) (2025)

## Abstract (arXiv)

Missing data is a common problem in time series data. Most methods for imputation ignore label information pertaining to the time series even if that information exists. In this paper, we provide a framework for missing data imputation in the context of time series classification, where each time series is associated with a categorical label. We define a means of imputing missing values conditional upon labels, the method being guided by powerful, existing supervised models designed for high accuracy in this task. From each model, we extract a tree-based proximity measure from which imputation can be applied. We show that imputation using this method generally provides richer information leading to higher classification accuracies, despite the imputed values differing from the true values.