Forecasting Financial Returns Volatility: A GARCH-SVR Model
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DOI: 10.1007/s10614-019-09896-w
Abstract: Support vector regression (SVR) is a semiparametric estimation method that has been used extensively in the forecasting of financial time series volatility. In this paper, we seek to design a two-stage forecasting volatility method by… read more here.
Keywords: garch svr; svr models; svr; volatility ... See more keywords
Kernel Methods for Predicting Yields of Chemical Reactions.
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DOI: 10.1021/acs.jcim.1c00699
Abstract: The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used… read more here.
Keywords: quantum chemical; kernel methods; methods predicting; structure based ... See more keywords
In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach
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DOI: 10.3390/toxics10120746
Abstract: In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50).… read more here.
Keywords: svr models; toxicity nitroaromatic; toxicity; silico prediction ... See more keywords