Forecast with Forecasts: Diversity Matters

TL;DR

This paper proposes a forecast combination method that leverages forecast diversity and out-of-sample forecasts to improve accuracy, simplifying modeling while achieving superior results in point and interval forecasts.

Contribution

It introduces a novel approach focusing on forecast outputs rather than historical data for feature extraction, enhancing forecast combination performance.

Findings

Outperforms traditional methods in diverse datasets

Simplifies the forecast modeling process

Achieves superior point and interval forecast accuracy

Abstract

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.