Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, these two proposals (DrCIF and FreshPRINCE) models are the only ones that significantly outperform the standard rotation forest regressor.

翻译：时间序列外生回归（TSER）涉及利用一组训练时间序列构建预测模型，以预测与回归变量序列无直接关联的连续响应变量。用于算法对比的TSER数据集档案于2022年发布，包含19个问题。我们将该档案规模扩展至63个问题，并复现了此前基线算法的比较结果。随后，我们扩大了比较范围，纳入了更广泛的标准回归器以及先前研究中使用的TSER模型的最新版本。研究结果表明，此前评估的所有回归器均无法超越标准分类器旋转森林的回归适配版本。我们基于时间序列分类领域的相关工作，提出了两种新型TSER算法：FreshPRINCE是一种管道式估计器，通过将时间序列转换为多维度统计特征后结合旋转森林回归器实现预测；DrCIF则是一种树集成方法，通过随机时间区间内的统计量构造特征。实验证明，这两种算法与InceptionTime模型均表现出显著优于其他18个测试回归器的性能。更关键的是，DrCIF与FreshPRINCE是唯一能够显著超越标准旋转森林回归器的两种模型。