Sunspot Data Denoising using Wavelet
M. Bindusri1, S. Koteswara Rao2
1M. Bindusri, Department of Electronics and Communication Engineering, KLEF Deemed University, Guntur (A.P), India.
2Dr. S. Koteswara Rao, Department of Electronics and Communication Engineering, KLEF Deemed University, Vaddeswaram (A.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 230-236 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2724028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In data analysis, signal processing plays a prominent role since the received sunspot data continuously fluctuates. Sunspot number data is corrupted with Gaussian noise and for statistical analysis; the noise needs to be filtered using wavelet transform. Traditional methods, Fourier transform and Kalman filter has limitations when analyzing the sunspot number data. A Wavelet transform is a promising tool that provides the time-frequency representation of the data. Daily sunspot number data from 2001 to 2018 is analyzed using Daubechies wavelet transform. Daubechies wavelet transform provides flexibility and is used for wide ranges of data using different denoising techniques such as Rigrsure, Sqtwolog, Heursure, Minimaxi thresholding methods. Results showed Sqtwolog (Universal (or) global threshold) and Heursure gave the better- denoised results compared with the other two denoising threshold methods for the sunspot number data.
Keyword: Denoising Methods- Heursure, Minimaxi, Rigrsure, Sqtwolog, Sunspot Number, Wavelets.
Scope of the Article: Data Mining Methods