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A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 5 No. 2 (2011)

DOI
10.22146/ijccs.2020
ISSN
2460-7258, 1978-1520
Published
2011-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Abstract— Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction. Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola’s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed. The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola’s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method. Keywords— fuzzy, wavelet, time series, soft computing

Keywords

Collaboration network of the authors

16 authors · 18 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Multimedia Nusantara University
  • Universitas Gadjah Mada
  • Institut Bisnis dan Teknologi Indonesia
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Udayana University
  • State University of Medan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Elena YustinaUniversitas Gadjah Mada1
Desak Made Dwi Utami PutraInstitut Bisnis dan Teknologi Indonesia1
Sylvia Jane Annatje SumarauwAffiliation unknown1
Shofwatul UyunSunan Kalijaga State Islamic University Yogyakarta1
I Wayan SuprianaUdayana University1
Siska Dewi LestariAffiliation unknown1
Zulfahmi IndraState University of Medan1
Dharma PratamaMultimedia Nusantara University1
Jason ChristianMultimedia Nusantara University1
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Azhari AzhariAffiliation unknown1
Santo Sinar PandeanMultimedia Nusantara University1
Naufal Irfan HayantoMultimedia Nusantara University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis</dc:title>
	<dc:creator>Hansun, Seng</dc:creator>
	<dc:creator>Subanar, Subanar</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">fuzzy, wavelet, time series, soft computing</dc:subject>
	<dc:description xml:lang="en-US">      Abstract— Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction.      Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola’s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed.      The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola’s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method. Keywords—  fuzzy, wavelet, time series, soft computing</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">IndoCEISS</dc:contributor>
	<dc:date>2011-07-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/2020</dc:identifier>
	<dc:identifier>10.22146/ijccs.2020</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 5, No 2 (2011): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/2020/1824</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2011 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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