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A Framework for Analysing Dynamic Communities in Large-scale Social Networks.

, , and . ICEIS (1), page 235-242. SciTePress, (2015)

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Arbitrage of forecasting experts., , , and . Mach. Learn., 108 (6): 913-944 (2019)A case study comparing machine learning with statistical methods for time series forecasting: size matters., , and . J. Intell. Inf. Syst., 59 (2): 415-433 (2022)Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with LightGBM., , and . PRICAI (1), volume 13031 of Lecture Notes in Computer Science, page 454-465. Springer, (2021)autoBagging: Learning to Rank Bagging Workflows with Metalearning., , , and . AutoML@PKDD/ECML, volume 1998 of CEUR Workshop Proceedings, page 21-27. CEUR-WS.org, (2017)Ensembles for Time Series Forecasting. University of Porto, Portugal, (2019)Unsupervised Concept Drift Detection Using a Student-Teacher Approach., , and . DS, volume 12323 of Lecture Notes in Computer Science, page 190-204. Springer, (2020)Arbitrated Ensemble for Time Series Forecasting., , , and . ECML/PKDD (2), volume 10535 of Lecture Notes in Computer Science, page 478-494. Springer, (2017)How to evaluate sentiment classifiers for Twitter time-ordered data?, , , and . CoRR, (2018)Evaluating time series forecasting models: an empirical study on performance estimation methods., , and . Mach. Learn., 109 (11): 1997-2028 (2020)Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes., , and . Mach. Learn., 112 (11): 4409-4430 (November 2023)