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18 results on '"Premanode, Bhusana"'

1. Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Author

Premanode, Bhusana, Meade, Nigel, and Toumazou, Christofer

Subjects
621.3
Abstract

Volatility is a key 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 an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of 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 proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.

Published
2014
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11. Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Author

Premanode, Bhusana, Meade, Nigel, and Toumazou, Christofer

Subjects
Statistics::Theory, Statistics::Applications, Statistics::Methodology
Abstract

Volatility is a key 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 an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of 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 proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression. Open Access

Published
2013

17. An Integrated ISFETs Instrumentation System in Standard CMOS Technology.

Author

Wai Pan Chan, Premanode, Bhusana, and Toumazou, Christofer

Subjects
COMPLEMENTARY metal oxide semiconductors, FIELD-effect transistors, ENERGY consumption, ELECTRONIC modulators, FM radio receivers
Abstract

This paper describes an integrated ISFETs instrumentation system in a 0.18 µm 1-poly-6-metal CMOS process. The chip is able to compute the average of CMOS ISFETs' threshold voltages by using an averaging array employing global negative current feedback. In addition, neither reference voltage nor current is required to set up the sigma-delta modulator because the internal signal is converted and processed in the frequency domain. The chip operates at 3.3 V for the analog blocks and the digital input/output blocks, and at 1.8 V for the core digital logic. It achieves 8 bits accuracy under 80 µW static power consumption. The die area is 2.6 mm². [ABSTRACT FROM AUTHOR]

Published
2010
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18. Effect of Alpinia galanga extract on cartilage degradation and on gene expression in human chondrocyte and synovial fibroblast metabolism.

Author

Pothacharoen, Peraphan, Choocheep, Kanyamas, Pitak, Tanyaluck, Pompimon, Wilart, Premanode, Bhusana, Hardingham, Timothy, and Kongtawelert, Prachya

Abstract

We investigated the effects of A. galanga extract on metabolism and gene expression involved in the interleukin-1 β (IL-1 β) response of human chondrocyte and synovial fibroblast. A. galanga extract inhibited IL-1 β enhanced matrix breakdown of the cartilage explants in a dose-dependent manner. It suppressed uronic acid loss from the tissue and decreased the release of sulfated GAG and hyaluronan into the medium. MMP-2 and MMP-9 activity in the culture medium of chondrosarcomas and synovial fibroblasts were significantly reduced in the presence of A. galanga extract, which also suppressed the production of MMP-1,-3 and-13. The A. galanga extract also significantly increased type II collagen, SOX9 and aggrecan gene expression, suggesting an ability to enhance anabolic activity. At a high dose of A. galanga extract there was a down-regulation of aggrecan gene expression. Comparison with Diacerein® showed its general anti-inflammatory potential to be similar. The A. galanga extract was shown to inhibit IL-1 β-stimulated cartilage matrix degradation in both systems. Additionally, the extract showed the potential to up-regulate certain chondrocyte anabolic genes. It may, therefore, offer some cartilage protective and anti-inflammatory properties as a therapeutic agent in arthritis. [ABSTRACT FROM AUTHOR]

Published
2006
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