Time series analysis with explanatory variables: A systematic literature review

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.