EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

TL;DR

This paper introduces EMD-regression, a novel method that decomposes data into multiple scales to improve modeling of non-stationary environmental time series, demonstrated on weather-related cardiovascular mortality with superior results.

Contribution

The paper presents a new EMD-regression approach that handles non-stationarity and uncovers multi-scale relationships, outperforming classical models in weather-health studies.

Findings

Humidity influences mortality at the monthly scale.

EMD-regression outperforms generalized additive and distributed lag models.

Provides detailed insights into multi-scale environmental effects.

Abstract

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.