Time series analysis with explanatory variables: A systematic literature review. (September 2018)

• Record Type: Journal Article
• Authors: Maçaira, Paula Medina; Tavares Thomé, Antônio Marcio; Cyrino Oliveira, Fernando Luiz; Carvalho Ferrer, Ana Luiza
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 199
• Page End: 209
• Publication Date: 2018-09
• Subjects: Regression analysis -- Artificial intelligence -- Exogenous variables -- Forecast scenarios; 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.2018.06.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Time series analysis with explanatory variables encompasses methods to model and predict correlated data taking into account additional information, known as exogenous variables. A thorough search in literature returned a dearth of systematic literature reviews (SLR) on time series models with explanatory variables. The main objective is to fill this gap by applying a rigorous and reproducible SLR and a bibliometric analysis to study the evolution of this area over time. The study resulted in the identification of the main methods of time series that incorporate input variables per knowledge area and methodology. The largest number of papers belongs to environmental sciences, followed by economics and health. Regression model is the method with the highest number of applications, followed by Artificial Neural Networks and Support Vector Machines, which experienced rapid and recent growth. A research agenda in time series analysis with exogenous variables closes the paper. Highlights: Use of time series with explanatory variables (TSEV) is increasing. Main TSEV areas of application are environmental sciences, economy, and health. Main methods are Regression, Artificial Neural Networks and Support Vector Machines. TSEV contribute to improve forecast accuracy. The choice of exogenous variables, methods and scenario simulation are paramount.