# The Generalized Proximity Forest

arXiv: https://arxiv.org/abs/2511.19487 · HTML: https://mlsanity.com/paper/2511.19487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [geomstats: a Python Package for Riemannian Geometry in Machine Learning](https://mlsanity.com/paper/1805.08308/) (2018)
- [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)
- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs](https://mlsanity.com/paper/2302.00713/) (2023)
- [Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities](https://mlsanity.com/paper/2307.01077/) (2023)
- [Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification](https://mlsanity.com/paper/2509.22919/) (2025)
- [STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades](https://mlsanity.com/paper/2509.24151/) (2025)

## Abstract (arXiv)

Recent work has demonstrated the utility of Random Forest (RF) proximities for various supervised machine learning tasks, including outlier detection, missing data imputation, and visualization. However, the utility of the RF proximities depends upon the success of the RF model, which itself is not the ideal model in all contexts. RF proximities have recently been extended to time series by means of the distance-based Proximity Forest (PF) model, among others, affording time series analysis with the benefits of RF proximities. In this work, we introduce the generalized PF model, thereby extending RF proximities to all contexts in which supervised distance-based machine learning can occur. Additionally, we introduce a variant of the PF model for regression tasks. We also introduce the notion of using the generalized PF model as a meta-learning framework, extending supervised imputation capability to any pre-trained classifier. We experimentally demonstrate the unique advantages of the generalized PF model compared with both the RF model and the k-nearest neighbors model.