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.
By Feng Li
Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. Feng obtained his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. His research interests include Bayesian computation, econometrics and forecasting, and distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.View all of Feng Li's posts.