# Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery

arXiv: https://arxiv.org/abs/2202.02253 · HTML: https://mlsanity.com/paper/2202.02253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Classification accuracy as a proxy for two sample testing](https://mlsanity.com/paper/1602.02210/) (2016)
- [Gene Hunting with Knockoffs for Hidden Markov Models](https://mlsanity.com/paper/1706.04677/) (2017)
- [Global and Local Two-Sample Tests via Regression](https://mlsanity.com/paper/1812.08927/) (2018)
- [Multivariate f-Divergence Estimation With Confidence](https://mlsanity.com/paper/1411.2045/) (2014)
- [Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series](https://mlsanity.com/paper/2004.14733/) (2020)
- [Two-Sample Testing for Event Impacts in Time Series](https://mlsanity.com/paper/2001.11930/) (2020)

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

- [Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)](https://mlsanity.com/paper/2205.14568/) (2022)

## Abstract (arXiv)

Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this question, we propose a new nonparametric test of association between a time series of images and a series of binary event labels. We ask whether there is a difference in distribution between (dependent but identically distributed) 24-h sequences of images preceding an event versus a non-event. By rewriting the statistical test as a regression problem, we leverage neural networks to infer modes of structural evolution of TC convection that are representative of the lead-up to rapid intensity change events. Dependencies between nearby sequences are handled by a bootstrap procedure that estimates the marginal distribution of the label series. We prove that type I error control is guaranteed as long as the distribution of the label series is well-estimated, which is made easier by the extensive historical data for binary TC event labels. We show empirical evidence that our proposed method identifies archetypes of infrared imagery associated with elevated rapid intensification risk, typically marked by deep or deepening core convection over time. Such results provide a foundation for improved forecasts of rapid intensification.