A forecasting ensemble consisting of a diverse range of estimators for both local and global univariate forecasting, in particular MQ-CNN,DeepAR, Prophet, NPTS, ARIMA and ETS, can be used to make forecasts for a variety of problems. This paper delves into the aspect of adding different hyperparameter optimization strategies to the deep learning models in such a setup (DeepAR and MQ-CNN), exploring the trade-off between added training cost and the increase in accuracy for different configurations. It shows that in such a setup, adding hyperparameter optimization can lead to performance improvements, with the final setup having a 9.9 % percent accuracy improvement with respect to the avg-wQL over the baseline ensemble without HPO, accompanied by a 65.8 % increase in end-to-end ensemble latency. This improvement is based on an empirical analysis of combining the ensemble pipeline with different tuning strategies, namely Bayesian Optimisation and Hyperband and different configurations of those strategies. In the final configuration, the proposed combination of ensemble learning and HPO outperforms the state of the art commercial AutoML forecasting solution, Amazon Forecast, with a 3.5 % lower error and 16.0 % lower end-to-end ensemble latency.

翻译：由多种局部和全局单变量预测估计器（特别是MQ-CNN、DeepAR、Prophet、NPTS、ARIMA和ETS）组成的预测集成，可用于解决各类预测问题。本文深入探讨了在该框架下为深度学习模型（DeepAR和MQ-CNN）添加不同超参数优化策略的问题，分析了不同配置下训练成本增加与精度提升之间的权衡关系。研究表明，在此类框架中添加超参数优化可带来性能提升：最终配置相对于未采用HPO的基准集成，其平均加权分位数损失（avg-wQL）提高了9.9%，而端到端集成延迟增加了65.8%。该改进基于对集成管道与不同调优策略（即贝叶斯优化和Hyperband及其变体配置）组合的实证分析。在最终配置中，所提出的集成学习与HPO组合方案优于业界领先的商业AutoML预测解决方案Amazon Forecast，误差降低3.5%，端到端集成延迟降低16.0%。