Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression

TL;DR

This paper expands the TSER archive to 63 problems, compares multiple regressors, and introduces two new algorithms, FreshPRINCE and DrCIF, which outperform existing methods and standard rotation forest models.

Contribution

It extends the TSER benchmark, evaluates a broad range of regressors, and proposes two novel algorithms that significantly improve time series extrinsic regression performance.

Findings

FreshPRINCE and DrCIF outperform other regressors.

Both new algorithms significantly outperform the standard rotation forest.

The expanded archive provides a more comprehensive benchmark for TSER algorithms.

Abstract

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.