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LAP Lambert Academic Publishing Prediction of nonlinear nonstationary time series data: A Digital Filter and Support Vector Regression

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£60.00 £54.50 £55.70 £56.90 £58.10 £59.30 £60.50 09 July 2026 25 July 2026 11 August 2026 28 August 2026 14 September 2026

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Description

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.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
14 June 2016
Listed Since
07 July 2016

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