Forecasting performance of denoising signal by Wavelet and Fourier Transforms using SARIMA model
The goal of this research is to determine the forecasting performance of denoising signal. Monthly rainfall and monthly number of raindays with duration of 20 years (1990-2009) from Bayan Lepas station are utilized as the case study. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are us...
| Main Authors: | Ismail, M.T., Mamat, S.S., Hamzah, F.M., Karim, S.A.A. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.32314 / |
| Published: |
American Institute of Physics Inc.
2014
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904601016&doi=10.1063%2f1.4887720&partnerID=40&md5=3ee6487d62dae5da402a280d4bf708a8 http://eprints.utp.edu.my/32314/ |
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| Summary: |
The goal of this research is to determine the forecasting performance of denoising signal. Monthly rainfall and monthly number of raindays with duration of 20 years (1990-2009) from Bayan Lepas station are utilized as the case study. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used in this research to find the denoise signal. The denoise data obtained by Fast Fourier Transform and Wavelet Transform are being analyze by seasonal ARIMA model. The best fitted model is determined by the minimum value of MSE. The result indicates that Wavelet Transform is an effective method in denoising the monthly rainfall and number of rain days signals compared to Fast Fourier Transform. © 2014 AIP Publishing LLC. |
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