  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 combination 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
modelling 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 optimisation and a decision analysis
perspective.
