Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)

Interval Combination Forecast Model Based on Arithmetic Average Approach Degree

Authors
Fengxiao Wang
Corresponding Author
Fengxiao Wang
Available Online March 2017.
DOI
10.2991/emcs-17.2017.321How to use a DOI?
Keywords
Interval combination forecast; Arithmetic average approach degree; Superior interval combination forecasting
Abstract

Using the arithmetic average minimum approach degree as a weighted approach degree, the new indicator of the relevance of the optimal interval combination forecast model is established. Some new concepts such as superior interval combination forecast model and non-inferior interval combination forecast model, which is on the basis of interval combination forecasting model based on arithmetic average degree are proposed. Some sufficient conditions of non-inferior and superior combination forecasting are given. Finally, Application example shows that this method can be effective for better forecast.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)
Series
Advances in Computer Science Research
Publication Date
March 2017
ISBN
978-94-6252-335-7
ISSN
2352-538X
DOI
10.2991/emcs-17.2017.321How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fengxiao Wang
PY  - 2017/03
DA  - 2017/03
TI  - Interval Combination Forecast Model Based on Arithmetic Average Approach Degree
BT  - Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)
PB  - Atlantis Press
SP  - 1682
EP  - 1687
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcs-17.2017.321
DO  - 10.2991/emcs-17.2017.321
ID  - Wang2017/03
ER  -