Searching for optimal variables in real multivariate stochastic data

TL;DR

This paper applies a stochastic eigendirection technique to analyze NO2 concentration data from Lisbon, revealing independent stochastic sources and proposing transformations to reduce stochasticity, which impacts prediction capabilities.

Contribution

It introduces a method to transform coupled stochastic variables into derived variables with reduced stochasticity using stochastic eigendirections.

Findings

Stochastic sources at each station are independent.

Derived variables can be approximated by a global rotation.

Limitations exist in predicting NO2 concentrations due to stochasticity.

Abstract

By implementing a recent technique for the determination of stochastic eigendirections of two coupled stochastic variables, we investigate the evolution of fluctuations of NO2 concentrations at two monitoring stations in the city of Lisbon, Portugal. We analyze the stochastic part of the measurements recorded at the monitoring stations by means of a method where the two concentrations are considered as stochastic variables evolving according to a system of coupled stochastic differential equations. Analysis of their structure allows for transforming the set of measured variables to a set of derived variables, one of them with reduced stochasticity. For the specific case of NO2 concentration measures, the set of derived variables are well approximated by a global rotation of the original set of measured variables. We conclude that the stochastic sources at each station are independent from each other and typically have amplitudes of the order of the deterministic contributions. Such findings show significant limitations when predicting such quantities. Still, we briefly discuss how predictive power can be increased in general in the light of our methods.