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 (déjà vu). Then, 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. In this manner, “forecasting with similarity” is a data-centric approach that tackles model uncertainty without depending on statistical forecasting models. We evaluate the approach using a rich collection of real data and show that it results in good forecasting accuracy, especially for yearly series.