The comparative forecast performance of univariate and multivariate models: an application to time series forecasting

Abstract

Time series forecasting is a major challenge in many real world applications such as stock price analysis, electricity prices, natural rubber prices and flood forecasting. This type of forecasting is to predict the values of a continuous variable (called as response variable or output variable) with a forecasting model based on historical data. There are two types of time series forecasting modeling methods; univariate and multivariate.