Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
This work addresses the need for improved regression models in time series analysis, though it is incremental as it builds on existing classification techniques and benchmarks.
The authors tackled the problem of Time Series Extrinsic Regression (TSER) by expanding the benchmark archive from 19 to 63 problems and evaluating algorithms, showing that two new methods, FreshPRINCE and DrCIF, significantly outperform 18 other regressors and the previous best, rotation forest.
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.