# Separating an Outlier from a Change

## TL;DR

This paper introduces a method for change detection that distinguishes true regime shifts from outliers in data with unknown post-change distributions, validated on economic and climate datasets.

## Contribution

It characterizes likely outlier events and proposes a distribution testing approach, improving detection accuracy over existing methods like FMA and GLRT.

## Key findings

- Successfully detects regime shifts in market and climate data
- Outperforms traditional tests in various performance metrics
- Identifies climate change as a significant regime shift

## Abstract

We study the change detection problem with an unknown post-change distribution. Under this constraint, the unknown change in the distribution of observations may occur in many ways without much structure on the observations, whereas, before the change point, a false alarm (outlier) is highly structured, following a particular sample path. We first characterize these likely events for the deviation and propose a method to test the empirical distribution, relative to the most likely way for it to occur as an outlier. We benchmark our method with finite moving average (FMA) and generalized likelihood ratio tests (GLRT) under 4 different performance criteria including the run time time complexity. Finally, we apply our method on economic market indicators and climate data. Our method successfully captures the regime shifts during times of historical significance for the markets and identifies the current climate change phenomenon to be a highly likely regime shift rather than a random event.