Forecasting with Big Data Using Global Forecasting Models

Forecasting models that are trained across sets of many time series, known as global forecasting models, have recently shown promising results in prestigious forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. This chapter provides insights on why global models are important for forecasting in the context of Big Data and how these models outperform traditional univariate models, in the presence of large collections of related time series. Furthermore, we explain the data preparation steps of global model fitting and provide a brief history of the evolution of global models over the past few years. We also cover the recent theoretical discussions and intuitions around global models and share a summary of open-source frameworks available to implement global models.