Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector Regression
Volatility is a critical parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving-Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays a significant role in predicting nonlinear and nonstationary time series data. The book contains a new class model comprised a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates.
yazar | Bhusana Premanode |
---|---|
Boyutlar ve boyutlar | 15 x 1,2 x 22 cm |
Tarafından yayınlandı | 14 Haziran 2016 |
Additional Contributors 15 x 0,5 x 22 cm WADE H MCCREE 18,9 x 0,5 x 24,6 cm 28 Ekim 2011 Mdpi AG 30 Ekim 2011 18,9 x 0,2 x 24,6 cm ERWIN N GRISWOLD 29 Ekim 2011 1 Ocak 2017 18,9 x 0,4 x 24,6 cm 28 Şubat 2018 3 Ocak 2017 18,9 x 0,3 x 24,6 cm 18,9 x 0,6 x 24,6 cm ROBERT H BORK Kolektif
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Sürüm ayrıntıları
yazar | Bhusana Premanode |
---|---|
isbn 10 | 3659894087 |
isbn 13 | 978-3659894084 |
Yayımcı | LAP LAMBERT Academic Publishing |
Dilim | İngilizce |
Boyutlar ve boyutlar | 15 x 1,2 x 22 cm |
Tarafından yayınlandı Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector Regression | 14 Haziran 2016 |
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