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- W2079005304 abstract "In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series." @default.
- W2079005304 created "2016-06-24" @default.
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- W2079005304 date "2014-01-01" @default.
- W2079005304 modified "2023-10-12" @default.
- W2079005304 title "A study of stationarity in time series by using wavelet transform" @default.
- W2079005304 cites W1522237958 @default.
- W2079005304 doi "https://doi.org/10.1063/1.4887692" @default.
- W2079005304 hasPublicationYear "2014" @default.
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