BibTex Citation Data :
@article{J.Gauss16132, author = {Dyah Kusumaningrum and Suparti Suparti and Di Maruddani}, title = {ANALISIS DATA RUNTUN WAKTU MENGGUNAKAN METODE WAVELET THRESHOLDING DENGAN MAXIMAL OVERLAP DISCRETE TRANSFORM}, journal = {Jurnal Gaussian}, volume = {6}, number = {1}, year = {2017}, keywords = {Multiresolution Analysis, Wavelet Thresholding Estimator}, abstract = { Wavelet is a mathematical tool for analyzing time series data. Wavelet has certain properties one of which is localized in the time domain and frequency and form an orthogonal basis in the space L 2 (R). There are two types of wavelet estimators are linear and nonlinear wavelet estimators. Linear wavelet estimators can be analyzed using the approach of Multiresolution Analysis (MRA), while nonlinear wavelet estimator called Wavelet Thresholding. Wavelet thresholding are emphasizing the reconstruction of wavelet using a number of the largest coefficient or can be said that only coefficient greater than a value taken, while other coefficients are ignored. There’re several factors that affect the smooth running of the estimation are the type of wavelet function, types of functions of thresholding, thresholding parameters, and the level of resolution. Therefore, in this thesis will have optimal threshold value in analyzing the data. Wavelet Thresholding method provides value of Mean Square Error (MSE) that smaller compare to wavelet method with the approach Multiresolution Analysis (MRA). In this case study Wavelet Thresholding are considered better in the analysis of time series data. Keywords: Multiresolution Analysis, Wavelet Thresholding Estimator. }, issn = {2339-2541}, pages = {151--159} doi = {10.14710/j.gauss.6.1.151-159}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/16132} }
Refworks Citation Data :
Wavelet is a mathematical tool for analyzing time series data. Wavelet has certain properties one of which is localized in the time domain and frequency and form an orthogonal basis in the space L2(R). There are two types of wavelet estimators are linear and nonlinear wavelet estimators. Linear wavelet estimators can be analyzed using the approach of Multiresolution Analysis (MRA), while nonlinear wavelet estimator called Wavelet Thresholding. Wavelet thresholding are emphasizing the reconstruction of wavelet using a number of the largest coefficient or can be said that only coefficient greater than a value taken, while other coefficients are ignored. There’re several factors that affect the smooth running of the estimation are the type of wavelet function, types of functions of thresholding, thresholding parameters, and the level of resolution. Therefore, in this thesis will have optimal threshold value in analyzing the data. Wavelet Thresholding method provides value of Mean Square Error (MSE) that smaller compare to wavelet method with the approach Multiresolution Analysis (MRA). In this case study Wavelet Thresholding are considered better in the analysis of time series data.
Keywords: Multiresolution Analysis, Wavelet Thresholding Estimator.
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