Multiresolution analysis of S&P500 time series

Kılıç, Deniz Kenan
Time series analysis is an essential research area for almost all people who are dealing with scientific and engineering problems. Main aim is to understand the underlying characteristics of the time series by using time as well as frequency domain analyses. Then one can make a prediction for the desired system to forecast observations ahead. Time series modeling, frequency domain analysis and some descriptive statistical analysis are main subjects of this thesis. Choosing an appropriate model is the main focus of all analysis in order to make a good prediction. In this thesis financial time series are focused, particularly S&P500 daily closing prices and it’s return values are handled. Fourier transform and wavelet transform are creatively at the center of the frequency domain analysis. Knowing the fact that financial time series are complex data sets to sufficiently predict the future, multiresolution analysis is handled in this thesis using the wavelet transforms to figure out specialties of S&P500 data. Also, apparently, models that are appropriate for the financial time series are discussed in the application part.


Multiresolution analysis of S&P500 time series
KILIC, Deniz Kenan; Uğur, Ömür (2018-01-01)
Time series analysis is an essential research area for those who are dealing with scientific and engineering problems. The main objective, in general, is to understand the underlying characteristics of selected time series by using the time as well as the frequency domain analysis. Then one can make a prediction for desired system to forecast ahead from the past observations. Time series modeling, frequency domain and some other descriptive statistical data analyses are the primary subjects of this study: i...
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Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3)
The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
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Citation Formats
D. K. Kılıç, “Multiresolution analysis of S&P500 time series,” M.S. - Master of Science, Middle East Technical University, 2015.