A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Liu, G; Xiao, F; Lin, CT; Cao, Z

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Liu, G Xiao, F Lin, CT Cao, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2020, 28, (11), pp. 2677-2690
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Liu, G
dc.contributor.author	Xiao, F
dc.contributor.author	Lin, CT
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date.accessioned	2020-11-21T19:09:44Z
dc.date.available	2020-11-21T19:09:44Z
dc.date.issued	2020-11-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2020, 28, (11), pp. 2677-2690
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/144207
dc.description.abstract	© 1993-2012 IEEE. Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2020.2972823
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation
dc.type	Journal Article
utslib.citation.volume	28
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-21T19:09:38Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	11

Abstract:

© 1993-2012 IEEE. Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

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http://hdl.handle.net/10453/144207