A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis
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.
Penulis: Seng Hansun, Subanar
Kode Jurnal: jptinformatikadd110165
