TY - GEN

T1 - Building an Ensemble of Time Series Models Using Empirical Risk Space

AU - Litvinenko, Dmitriy

AU - Berikov, Vladimir

PY - 2024

Y1 - 2024

N2 - This paper presents a novel approach to building ensembles of time series models within the framework of empirical risk space. By conducting an in-depth analysis of the errors made by individual experts, particularly in relation to specific features of the data, the proposed method effectively optimizes expert weights through a sophisticated aggregation algorithm. This approach not only incorporates the concept of expert specialization but also meticulously considers the feature- specific manifestations of errors to accurately identify and exclude experts exhibiting consistent erroneous behavior. Experimental results demonstrate significant improvements in prediction accuracy when compared to traditional ensemble methods. These findings contribute to the advancement of ensemble modeling techniques and underscore the critical importance of feature-specific error analysis in the construction of robust time series ensembles.

AB - This paper presents a novel approach to building ensembles of time series models within the framework of empirical risk space. By conducting an in-depth analysis of the errors made by individual experts, particularly in relation to specific features of the data, the proposed method effectively optimizes expert weights through a sophisticated aggregation algorithm. This approach not only incorporates the concept of expert specialization but also meticulously considers the feature- specific manifestations of errors to accurately identify and exclude experts exhibiting consistent erroneous behavior. Experimental results demonstrate significant improvements in prediction accuracy when compared to traditional ensemble methods. These findings contribute to the advancement of ensemble modeling techniques and underscore the critical importance of feature-specific error analysis in the construction of robust time series ensembles.

KW - - univariate time series

KW - ensembling

KW - prediction

KW - specialized experts

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85208267627&origin=inward&txGid=a2607c583c84e0017b4031f55c1ad9b0

UR - https://www.mendeley.com/catalogue/a3c44ada-6ad7-30db-8490-b5d7f2ddf89b/

U2 - 10.1109/RusAutoCon61949.2024.10694116

DO - 10.1109/RusAutoCon61949.2024.10694116

M3 - Conference contribution

SN - 979-8-3503-4982-5

SP - 751

EP - 756

BT - RusAutoCon - Proceedings of the International Russian Automation Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 International Russian Automation Conference

Y2 - 8 September 2024 through 14 September 2024

ER -