Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions. (November 2019)

• Record Type: Journal Article
• Authors: Mindham, David A.; Tych, Wlodzimierz
• Is Part Of: Environmental modelling & software. Volume 121(2019)
• Publication Date: 2019-11
• Subjects: Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104503
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Many environmental time-series measurements are characterised by irregular sampling. A significant improvement of the Dynamic Harmonic Regression (DHR) modelling technique to accommodate irregular sampled time-series, without the need for data pre-processing, has been developed. Taylor's series is used to obtain the time-step state increments, modifying the transition equation matrices. This allows the user to avoid artefacts arising and insertion of assumptions from interpolation and regularisation of the data to a regular time-base and makes DHR more consistent with the Data-Based Mechanistic approach to modelling environmental systems. The new technique implemented as a Matlab package has been tested on demanding simulated data-sets and demonstrated on various environmental time-series data with significantly varying sampling times. The results have been compared with standard DHR, where possible, and the method reduces analysis time and produces unambiguous results (by removing the need for pre-processing – always based on assumptions) based only on the observed environmental data. Highlights: Extension of Data-Based Mechanistic modelling using Unobserved Component Models for irregularly sampled environmental systems. Removing the need for data pre-processing e.g. interpolation and resampling avoiding processing artefacts. Application to irregularly sampled environmental systems, and in particular to paleo-climatic data and other proxy series.