Dynamic and heterogeneous ensembles for time series forecasting

Vitor Cerqueira; Luis Torgo; Mariana Oliveira; Bernhard Pfahringer

doi:10.1109/DSAA.2017.26

Dynamic and heterogeneous ensembles for time series forecasting

Vitor Cerqueira, Luis Torgo, Mariana Oliveira, Bernhard Pfahringer

Dentistry

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

23 Citations (Scopus)

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	242-251
Number of pages	10
ISBN (Electronic)	9781509050048
DOIs	https://doi.org/10.1109/DSAA.2017.26
Publication status	Published - Jul 2 2017
Event	4th International Conference on Data Science and Advanced Analytics, DSAA 2017 - Tokyo, Japan Duration: Oct 19 2017 → Oct 21 2017

Publication series

Name	Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
Volume	2018-January

Conference

Conference	4th International Conference on Data Science and Advanced Analytics, DSAA 2017
Country/Territory	Japan
City	Tokyo
Period	10/19/17 → 10/21/17

ASJC Scopus Subject Areas

Signal Processing
Information Systems and Management
Statistics, Probability and Uncertainty
Computer Networks and Communications

Access to Document

10.1109/DSAA.2017.26

Cite this

Cerqueira, V., Torgo, L., Oliveira, M., & Pfahringer, B. (2017). Dynamic and heterogeneous ensembles for time series forecasting. In Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017 (pp. 242-251). (Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2017.26

Dynamic and heterogeneous ensembles for time series forecasting. / Cerqueira, Vitor; Torgo, Luis; Oliveira, Mariana et al.
Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 242-251 (Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cerqueira, V, Torgo, L, Oliveira, M & Pfahringer, B 2017, Dynamic and heterogeneous ensembles for time series forecasting. in Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017. Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 242-251, 4th International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, 10/19/17. https://doi.org/10.1109/DSAA.2017.26

Cerqueira V, Torgo L, Oliveira M, Pfahringer B. Dynamic and heterogeneous ensembles for time series forecasting. In Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 242-251. (Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017). doi: 10.1109/DSAA.2017.26

Cerqueira, Vitor ; Torgo, Luis ; Oliveira, Mariana et al. / Dynamic and heterogeneous ensembles for time series forecasting. Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 242-251 (Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017).

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author = "Vitor Cerqueira and Luis Torgo and Mariana Oliveira and Bernhard Pfahringer",

note = "Funding Information: This work is financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Funda¸c{\~a}o para a Ci{\^e}ncia e a Tecnologia (Portuguese Foundation for Science and Technology) within project Fotocatgraf - UTAP-ICDT/CTM-NAN/0025/2014. This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Funda¸c{\~a}o para a Ci{\^e}ncia e a Tecnologia as part of project UID/EEA/50014/2013. Funding Information: This work is financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia (Portuguese Foundation for Science and Technology) within project Fotocatgraf - UTAP-ICDT/CTM-NAN/0025/2014. This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia as part of project UID/EEA/50014/2013. Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th International Conference on Data Science and Advanced Analytics, DSAA 2017 ; Conference date: 19-10-2017 Through 21-10-2017",

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N1 - Funding Information: This work is financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Funda¸cão para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project Fotocatgraf - UTAP-ICDT/CTM-NAN/0025/2014. This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Funda¸cão para a Ciência e a Tecnologia as part of project UID/EEA/50014/2013. Funding Information: This work is financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project Fotocatgraf - UTAP-ICDT/CTM-NAN/0025/2014. This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundação para a Ciência e a Tecnologia as part of project UID/EEA/50014/2013. Publisher Copyright: © 2017 IEEE.

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Dynamic and heterogeneous ensembles for time series forecasting

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ASJC Scopus Subject Areas

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