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Book synopsis

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.

Book details

• Full title: Prediction of nonlinear nonstationary time series data
• Subtitle: A Digital Filter and Support Vector Regression
• Author: Bhusana Premanode
• Language: English
• Binding: Paperback
• Number of pages: 212
• EAN: 9783659894084
• ID: 13515236
• Publisher: LAP Lambert Academic Publishing
• Weight: 318 g
• Dimensions: 229 × 152 × 12 mm
• Date of publishing: 14. June 2016

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