Selecting the right set of hyperparameters is crucial in time series forecasting. The classical temporal cross-validation framework for hyperparameter optimization (HPO) often leads to poor test performance because of a possible mismatch between validation and test periods. To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets. Since higher-level aggregated time series often show less irregularity and better predictability as compared to the lowest-level time series which can be sparse and intermittent, we optimize the hyperparameters of the lowest-level base-forecaster by leveraging the proxy forecasts for the test period generated from the forecasters at higher levels. H-Pro can be applied on any off-the-shelf machine learning model to perform HPO. We validate the efficacy of our technique with extensive empirical evaluation on five publicly available hierarchical forecasting datasets. Our approach outperforms existing state-of-the-art methods in Tourism, Wiki, and Traffic datasets, and achieves competitive result in Tourism-L dataset, without any model-specific enhancements. Moreover, our method outperforms the winning method of the M5 forecast accuracy competition.

翻译：超参数的正确选择对时间序列预测至关重要。经典的基于时序交叉验证的超参数优化框架，由于验证期与测试期可能存在不匹配，往往导致测试性能不佳。为解决这种测试-验证不匹配问题，我们提出了一种新颖技术H-Pro，通过利用时间序列数据中常见的层级关联性，借助测试代理来驱动超参数优化。鉴于相较于可能稀疏且不连续的底层时间序列，高层聚合时间序列通常表现出更小的不规则性与更强的可预测性，我们通过利用高层预测器在测试期生成的代理预测，来优化底层基预测器的超参数。H-Pro可应用于任何现成的机器学习模型以执行超参数优化。我们在五个公开的层级预测数据集上进行了广泛的实证评估，验证了该技术的有效性。我们的方法在Tourism、Wiki和Traffic数据集上超越了现有最先进方法，并在Tourism-L数据集上取得了具有竞争力的结果，且无需任何模型特定增强。此外，我们的方法还超越了M5预测精度竞赛的获胜方法。