Graduate School and Research Center in Digital Sciences

Predicting error metrics of time-series forecasting

Candela, Rosa; Zuluaga, Maria A.; Michiardi, Pietro; Filippone, Maurizio

Research disclosure database number: 664052; 16 July 2019

This disclosure discusses a data-driven framework to predict an error of time-series forecasting models. Monitoring and management of time-series forecasting models are crucial tasks in industrial and business contexts, where multiple models are deployed and used over time to guarantee their correct operation. However, the large amount of available data, together with the large availability of forecasting models complicate the performance of these tasks. A novel supervised learning approach is presented to predict the error of time-series forecasting models, exploiting information on the forecasting error on other time-series available at training time. The estimated forecasting error represents a surrogate measure of a model’s future performance and can be used to jointly perform model monitoring and selection over time.


Title:Predicting error metrics of time-series forecasting
Department:Data Science
Eurecom ref:6186
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Research disclosure database number: 664052; 16 July 2019 and is available at :
Bibtex: @patent{EURECOM+6186, year = {2019}, title = {{P}redicting error metrics of time-series forecasting}, author = {{C}andela, {R}osa and {Z}uluaga, {M}aria {A}. and {M}ichiardi, {P}ietro and {F}ilippone, {M}aurizio}, url = {} }
See also: