| Full text | |
| Author(s): |
Guijo-Rubio, David
;
Middlehurst, Matthew
;
Arcencio, Guilherme
;
Silva, Diego Furtado
;
Bagnall, Anthony
Total Authors: 5
|
| Document type: | Journal article |
| Source: | DATA MINING AND KNOWLEDGE DISCOVERY; v. 38, n. 4, p. 45-pg., 2024-05-19. |
| 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, DrCIF is the only one that significantly outperforms a standard rotation forest regressor. (AU) | |
| FAPESP's process: | 22/12486-4 - From tabular data to time series: novel algorithms for time series extrinsic regression |
| Grantee: | Guilherme Gomes Arcencio |
| Support Opportunities: | Scholarships abroad - Research Internship - Scientific Initiation |
| FAPESP's process: | 22/00305-5 - Adaptation of classification algorithms to the time series extrinsic regression task |
| Grantee: | Guilherme Gomes Arcencio |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| FAPESP's process: | 22/03176-1 - Machine learning for time series obtained in mHealth applications |
| Grantee: | Diego Furtado Silva |
| Support Opportunities: | Research Grants - Initial Project |