Ensembles of recurrent neural networks for robust time series forecasting

DOI:	https://doi.org/10.1007/978-3-319-71078-5_3
URL:	https://link.springer.com/chapter/10.1007/978-3-31...
Weitere URL:	http://www.heikopaulheim.com/docs/sgai_2017.pdf
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2017
Buchtitel:	Artificial Intelligence XXXIV : 37th SGAI International Conference on Artificial Intelligence, AI 2017, Cambridge, UK, December 12-14, 2017, proceedings
Titel einer Zeitschrift oder einer Reihe:	Lecture Notes in Computer Science
Band/Volume:	10630
Seitenbereich:	34-46
Veranstaltungstitel:	SGAI International Conference on Artificial Intelligence
Veranstaltungsort:	Cambridge, UK
Veranstaltungsdatum:	12.-14.12.2017
Herausgeber:	Bramer, Max
Ort der Veröffentlichung:	Berlin [u.a.]
Verlag:	Springer
ISBN:	978-3-319-71077-8 , 978-3-319-71078-5
ISSN:	0302-9743 , 1611-3349
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems V: Web-based Systems (Bizer 2012-) Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
Fachgebiet:	004 Informatik
Abstract:	Time series forecasting is a problem that is strongly dependent on the underlying process which generates the data sequence. Hence,finding good model fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or heavy parameter optimization. Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs), provide state of the art forecasting performance without prior assumptions about the data distribution. LSTMs are, however, highly sensitive to the chosen network architecture and parameter selection, which makes it difficult to come up with a one-size-fits-all solution without sophisticated optimization and parameter tuning. To overcome these limitations, we propose an ensemble architecture that combines forecasts of a number of differently parameterized LSTMs to a robust final estimate which, on average, performs better than the majority of the individual LSTM base learners, and provides stable results across different datasets. The approach is easily parallelizable and we demonstrate its effectiveness on several real-world data sets.