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Prediction of nonlinear nonstationary time series data
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Prediction of nonlinear nonstationary time series data Paperback -

by Bhusana Premanode

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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.

Details

  • Title Prediction of nonlinear nonstationary time series data
  • Author Bhusana Premanode
  • Binding Paperback
  • Pages 212
  • Volumes 1
  • Language ENG
  • Publisher LAP Lambert Academic Publishing
  • ISBN 9783659894084 / 3659894087
  • Weight 0.7 lbs (0.32 kg)
  • Dimensions 9 x 6 x 0.48 in (22.86 x 15.24 x 1.22 cm)
  • Category Mathematics

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Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector...
Stock photo: cover may vary

Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector Regression

by Premanode, Bhusana

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ISBN 10 / ISBN 13
9783659894084 / 3659894087
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paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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A$181.67
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Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector Regression

Premanode, Bhusana

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