# Forecast with Forecasts: Diversity Matters

arXiv: https://arxiv.org/abs/2012.01643 · HTML: https://mlsanity.com/paper/2012.01643/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Hierarchical forecasting with a top-down alignment of independent level forecasts](https://mlsanity.com/paper/2103.08250/) (2021)
- [Stacking for Probabilistic Short-term Load Forecasting](https://mlsanity.com/paper/2406.10718/) (2024)
- [Designing probabilistic AI monsoon forecasts to inform agricultural decision-making](https://mlsanity.com/paper/2603.07893/) (2026)
- [Forecasting large collections of time series: feature-based methods](https://mlsanity.com/paper/2309.13807/) (2023)
- [Optimizing accuracy and diversity: a multi-task approach to forecast combinations](https://mlsanity.com/paper/2310.20545/) (2023)
- [Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality](https://mlsanity.com/paper/2504.08940/) (2025)
- [Generalized Gibbs Ensemble Weighting for Forecast Combination](https://mlsanity.com/paper/2608.28116/) (2026)

## Abstract (arXiv)

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.