Déjà vu: forecasting with similarity

Accurate forecasts are vital for supporting the decisions of modern companies. To improve statistical forecasting performance, forecasters typically select the most appropriate model for each given time series. However, statistical models usually presume some data generation process, while making strong distributional assumptions about the errors. In this paper, we present a new approach to time series forecasting that relaxes these assumptions. A target series is forecasted by identifying similar series from a reference set (d\'ej\`a vu). Instead of extrapolating, the future paths of the similar reference series are aggregated and serve as the basis for the forecasts of the target series."Forecasting with similarity" is a data-centric approach that tackles model uncertainty without depending on statistical forecasting models.We offer definitions for deriving both the point forecasts and the corresponding prediction intervals.We evaluate the approach using a rich collection of real data and show that it results in good forecasting accuracy, especially for yearly series.Finally, while traditional statistical approaches underestimate the uncertainty around the forecasts, our approach results in upper coverage levels that are much closer to the nominal values.