# Nonlinearity in Data with Gaps: Application to Ecological and Meteorological Datasets

## TL;DR

This paper investigates how data gaps affect the measurement of multifractality in ecological and meteorological datasets, demonstrating that certain gap ranges do not distort nonlinearity quantification without interpolation.

## Contribution

It quantifies the impact of data gaps on multifractal spectra and identifies gap ranges that preserve nonlinear measures without interpolation, applied to real-world datasets.

## Key findings

- Identified tolerable gap ranges for multifractal analysis
- Established multifractality due to nonlinearity in datasets with gaps
- Demonstrated robustness of multifractal measures in ecological and meteorological data

## Abstract

Datagaps are ubiquitous in real world observational data. Quantifying nonlinearity in data having gaps can be challenging. Reported research points out that interpolation can affect nonlinear quantifiers adversely, artificially introducing signatures of nonlinearity where none exist. In this paper we attempt to quantify the effect that datagaps have on the multifractal spectrum ($f(\alpha)$), in the absence of interpolation. We identify tolerable gap ranges, where the measures defining the $f(\alpha)$ curve do not show considerable deviation from the evenly sampled case. We apply this to the multifractal spectra of multiple data-sets with missing data from the SMEAR database. The datasets we consider include ecological datasets from SMEAR I, namely CO$_2$ exchange variation, photosynthetically active radiation levels and soil moisture variation time series, and meteorological datasets from SMEAR II, namely dew point variation and air temperature variation. We could establish multifractality due to deterministic nonlinearity in two of these datasets, even in the presence of gaps.