Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series

Jiang, P; Liu, F; Wang, J; Song, Y

Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series

Jiang, P Liu, F

Wang, J Song, Y

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Applied Mathematical Modelling, 2016, 40, (23-24), pp. 9692-9718
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Jiang, P
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.contributor.author	Wang, J
dc.contributor.author	Song, Y https://orcid.org/0000-0002-6633-2695
dc.date.accessioned	2022-08-07T05:46:41Z
dc.date.available	2022-08-07T05:46:41Z
dc.date.issued	2016-12-01
dc.identifier.citation	Applied Mathematical Modelling, 2016, 40, (23-24), pp. 9692-9718
dc.identifier.issn	0307-904X
dc.identifier.issn	1872-8480
dc.identifier.uri	http://hdl.handle.net/10453/159729
dc.description.abstract	Reliable data have always played a vital role in time series analysis and research. Nevertheless, missing data, which can bias the original properties of the time series if the pattern of missed data is systematic, is also a very common phenomenon in observed processes. Thus, how to address missing values is a very important challenge, especially in the upcoming big data era. This paper proposes the cuckoo search-designated fractal interpolation functions (CS-DFIFs) method and the CS-DFIFs-winners combining (CS-DFIFs-WC) method for estimating missing values. The former method skillfully transforms Fractal Interpolation Functions (FIFs) to make it possible to calculate a specified point's missing value, which is difficult to obtain with the traditional approach. Then, to optimize the parameters that transforming process generates, the cuckoo search algorithm (CS) and DFIFs are synthesized into a novel model, CS-DFIFs. Considering classical interpolation methods, such as Linear, Cubic Spline and Piecewise Cubic Hermite Interpolation Polynomial (PCHIP), having some born advantages, the inspiration of winners combining is sparked. CS algorithm is used to obtain the best weights of winners in this combined model, CS-DFIFs-WC. Two databases, electricity demands and prices, are chosen to be the numerical testing object at 7 missing levels in this paper. The results show that CS-DFIFs-WC overcomes the deficiency of FIFs that cannot calculate a specified point's missing value, and outperforms benchmarks at almost every level by four criteria.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Applied Mathematical Modelling
dc.relation.isbasedon	10.1016/j.apm.2016.05.030
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Mechanical Engineering & Transports
dc.title	Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series
dc.type	Journal Article
utslib.citation.volume	40
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-07T05:46:34Z
pubs.issue	23-24
pubs.publication-status	Published
pubs.volume	40
utslib.citation.issue	23-24

Abstract:

Reliable data have always played a vital role in time series analysis and research. Nevertheless, missing data, which can bias the original properties of the time series if the pattern of missed data is systematic, is also a very common phenomenon in observed processes. Thus, how to address missing values is a very important challenge, especially in the upcoming big data era. This paper proposes the cuckoo search-designated fractal interpolation functions (CS-DFIFs) method and the CS-DFIFs-winners combining (CS-DFIFs-WC) method for estimating missing values. The former method skillfully transforms Fractal Interpolation Functions (FIFs) to make it possible to calculate a specified point's missing value, which is difficult to obtain with the traditional approach. Then, to optimize the parameters that transforming process generates, the cuckoo search algorithm (CS) and DFIFs are synthesized into a novel model, CS-DFIFs. Considering classical interpolation methods, such as Linear, Cubic Spline and Piecewise Cubic Hermite Interpolation Polynomial (PCHIP), having some born advantages, the inspiration of winners combining is sparked. CS algorithm is used to obtain the best weights of winners in this combined model, CS-DFIFs-WC. Two databases, electricity demands and prices, are chosen to be the numerical testing object at 7 missing levels in this paper. The results show that CS-DFIFs-WC overcomes the deficiency of FIFs that cannot calculate a specified point's missing value, and outperforms benchmarks at almost every level by four criteria.

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http://hdl.handle.net/10453/159729