# Time-series thresholding and the definition of avalanche size

## TL;DR

This paper reveals that thresholding in continuous time series can obscure true avalanche scaling behavior, leading to potential misinterpretations of neural activity data and false universality class assignments.

## Contribution

It demonstrates analytically and computationally how threshold choices affect avalanche size measurements and scaling analysis in continuous stochastic processes.

## Key findings

- Large thresholds can mask true power-law scaling.
- Transient effects dominate when using area-based avalanche sizes.
- Misinterpretations can lead to incorrect universality class identification.

## Abstract

Avalanches whose sizes and durations are distributed as power laws appear in many contexts. Here, we show that there is a hidden peril in thresholding continuous times series --either from empirical or synthetic data-- for the detection of avalanches. In particular, we consider two possible alternative definitions of avalanche size used e.g. in the empirical determination of avalanche exponents in the analysis of neural-activity data. By performing analytical and computational studies of an Ornstein-Uhlenbeck process (taken as a guiding example) we show that if (i) relatively large threshold values are employed to determine the beginning and ending of avalanches and if (ii) --as sometimes done in the literature-- avalanche sizes are defined as the total area (above zero) of the avalanche, then true asymptotic scaling behavior is not seen, instead the observations are dominated by transient effects. These can induce misinterpretations of the resulting scaling regimes as well as to a wrong assignation of universality classes.