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.