Forecasting Performance of Denoising Signal by Wavelet and Fourier Transforms using SARIMA Model

被引：1
作者：
Ismail, Mohd Tahir [1 ]
Mamat, Siti Salwana [1 ]
Hamzah, Firdaus Mohamad [2 ]
Karim, Samsul Ariffin Abdul [3 ]
机构：
[1] Univ Sains Malaysia, Sch Math Sci, Usm Minden 11800, Penang, Malaysia
[2] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Unit Fundamental Engn Studies, Bangi 43600, Malaysia
[3] Univ Teknol PETRONAS, Department Fundamental & Appl Sci, Tronoh 31750, Malaysia
来源：
PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): GERMINATION OF MATHEMATICAL SCIENCES EDUCATION AND RESEARCH TOWARDS GLOBAL SUSTAINABILITY | 2014年 / 1605卷
关键词：
Wavelet transform; Fourier transform; SARIMA model;
D O I：
10.1063/1.4887720
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页码：961 / 966
页数：6
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