​Forecast with Forecasts: Diversity Matters

Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li

Research output: Contribution to journalArticlepeer-review

9 Citations (SciVal)


Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combinations has flourished. Although this idea has been proved to be beneficial in several forecasting competitions, it may not be practical in many situations. For example, the task of selecting appropriate features to build forecasting models is often challenging. Even if there was an acceptable way to define the features, existing features are estimated based on the historical patterns, which are likely to change in the future. Other times, the estimation of the features is infeasible due to limited historical data. In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features. We use out-of-sample forecasts to obtain weights for forecast combinations by amplifying the diversity of the pool of methods being combined. A rich set of time series is used to evaluate the performance of the proposed method. Experimental results show that our diversity-based forecast combination framework not only simplifies the modeling process but also achieves superior forecasting performance in terms of both point forecasts and prediction intervals. The value of our proposition lies on its simplicity, transparency, and computational efficiency, elements that are important from both an optimization and a decision analysis perspective.
Original languageEnglish
Pages (from-to)180-190
Number of pages11
JournalEuropean Journal of Operational Research
Issue number1
Early online date18 Oct 2021
Publication statusPublished - 16 Aug 2022


  • Empirical evaluation
  • Forecast combination
  • Forecast diversity
  • Forecasting
  • Prediction intervals

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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