استخدام مرشح الموجة الصغيرة المتقطعة في تحليل السلسلة الزمنية AR (1) ومقارنته مع مرشحات اخرى == Using Discrete Wavelets Filter In Analysis of Time Series AR(1) And Comparison With Other Filters
Author name:
طه حسين علي الزبيدي
Supervisor name:
عبد المجيد حمزة الناصر
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T1707 - p.pdf
Abstract:
تم في هذه الاطروحة معالجة مشكلة الضوضاء (او التلوث) الذي يمكن ان تتعرض له مشاهدات السلسلة الزمنية باستخدام مرشحات الموجة الصغيرة والاعتماد على تحليل المعلومات من خلال التردد فضلا عن الزمن مقارنة مع تحليل فورير الذي يحلل المعلومات عن طريق التردد فقط مهم | This Thesis deals with the problem of Noise (or Contamination) which may encounter the time series data using Wavelet Filters and depending on the information analysis through the frequency in addition to the time compared to Fourier Analysis which analyze the information through the frequency only omitting the time factor, this was performed through the use of Discrete Wavelet Transformation as a filter to clean the data from the contamination or the noise factors by direct or with some kinds Thresholding , and then Estimate the First - order Autoregressive Model for the filtered time series observations and compare the results with what results from the use of time series observations that are contaminated and filtered by using Wiener and Kalman filters depending on some statistical Criterias, which are The Mean Square Prediction Error, Final Prediction Error, and The Mean Absolute Prediction Error.This study presents the suggested method as well, that depends on the Wavelet as Input for The Artificial Neural Network, and then use the outputs of this Network to Estimate The First - order Autoregressive Model to the time series observations that are filtered, and compare the results with the Classical Method - Neural Network, Haar Wavelet, and Daubechies Filters of the directs from second order and which used with Soft, Mid, and Hard Thresholding by depending on the statistical Criterias given before through using the simulation experiments in addition to use real data represents time series observations of sunspots, and in order to perform this analysis the researcher designed the required computer codes by using MATLAB Language.
