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

https://doi.org/10.22146/ijccs.2020

Seng Hansun(1*), Subanar Subanar(2),

(1) 
(2) 
(*) Corresponding Author

Abstract


      AbstractRecently, 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


fuzzy, wavelet, time series, soft computing

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References

[1]     Popoola, A.O., 2007, Fuzzy-Wavelet Method for Time Series Analysis, Disertasi, Department of Computing, School of Electronics and Physical Sciences, University of Surrey, Surrey.

 

[2]     Popoola, A., Ahmad, S. dan Ahmad, K., 2004, A Fuzzy-Wavelet Method for Analyzing Non-Stationary Time Series, Proc. of the 5th International Conference on Recent Advances in Soft Computing RASC2004, Nottingham, United Kingdom, 231-236.

 

[3]     Render, B., Stair Jr., R.M. dan Hanna, M.E., 2003, Quantitative Analysis for Management, 8th edition, Pearson Education, Inc., New Jersey.

 

[4]     Subanar dan Suhartono, 2009, Wavelet Neural Networks untuk Peramalan Data Time Series Finansial, Program Penelitian Ilmu Dasar Perguruan Tinggi, FMIPA UGM, Yogyakarta.

 

[5]     Fuller, R., 1995, Neural Fuzzy Systems, Abo, Finland.

 

[6]     Kusumadewi, S. dan Purnomo, H., 2004, Aplikasi Logika Fuzzy untuk Pendukung Keputusan, Graha Ilmu, Yogyakarta.

 

[7]     Klir, G.J. dan Yuan, B., 1995, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, Inc., United States of America.

 

[8]     Percival, D.B. dan Walden, A.T., 2000, Wavelet Methods for Time Series Analysis, Cambridge University Press, New York.

 

[9]     Chen, S.-M. dan Hsu, C.-C., 2004, A New Method to Forecast Enrollments Using Fuzzy Time Series, International Journal of Applied Science and Engineering, 2, 3, 234-244.

 

[10]   Stevenson, M. dan Porter, J.E., 2009, Fuzzy Time Series Forecasting Using Percentage Change as the Universe of Discourse, World Academy of Science, Engineering and Technology, 27, 55, 154-157, http://www.waset.org/journals/waset/v55/.



DOI: https://doi.org/10.22146/ijccs.2020

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Indonesian Journal of Computing and Cybernetics Systems
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