  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.
