Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms

This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in multiple time series prediction problems. Although some multiple time series algorithms perform surprisingly well in a variety of domains, i...

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Veröffentlicht in:	Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics S. 1060 - 1067
Hauptverfasser:	Mochinski, Marcos Alberto, Paul Barddal, Jean, Enembreck, Fabricio
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 11.10.2020
Schlagworte:	Data mining data stream mining algorithms Forecasting hybrid ensemble multiple time series Prediction algorithms Real-time systems Social networking (online) Time series analysis Time series forecasting Training
ISSN:	2577-1655
Online-Zugang:	Volltext
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