State-parameter dependency estimation of stochastic time series using data transformation and parameterization by support vector regression

机译：支持向量回归技术通过数据变换和参数化估计随机时间序列的状态参数依赖性

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This position paper is about the identification of the dependency among parameters and states in regression models of stochastic time series. Conventional recursive algorithms for parameter estimation do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear behavior. To detect this dependence using conventional algorithms, we are studying some data transformations that we implement in this paper. Non-parametric relationships among parameters and states are obtained and parameterized using support vector regression. This way we look for a final non-linear structure to solve the SDP identification problem.

机译：该立场文件是关于确定随机时间序列回归模型中参数和状态之间的依存关系的。用于参数估计的常规递归算法在具有状态相关参数（SDP）的模型中无法提供良好的结果，因为这些参数可能具有高度非线性的行为。为了使用常规算法检测这种依赖性，我们正在研究我们在本文中实现的一些数据转换。使用支持向量回归获得参数和状态之间的非参数关系并将其参数化。这样，我们寻找最终的非线性结构来解决SDP识别问题。

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《International Conference on Informatics in Control, Automation and Robotics》|2015年|342-347|共6页
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Alegria Elvis Omar Jara; Teixeira Hugo Tanzarella; Bottura Celso Pascoli;
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Computational modeling; Data models; Estimation; Sorting; Stochastic processes; Support vector machines; Time series analysis; State-dependent Parameter; Support Vector Regression; Time Series Identification;

机译：计算建模;数据模型;估计;排序;随机过程;支持向量机;时间序列分析;状态相关参数;支持向量回归;时间序列识别;

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1. Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana [J] . Ika Oktavianti, Ermatita Ermatita, Dian Palupi Rini Jurnal RESTI: Rekayasa Sistem dan Teknologi Informasi . 2019,第2期

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2. Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series [J] . Jakub Langhammer, Julius ?esák Water . 2016,第12期

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3. A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data [J] . Liu Jie, Vitelli Valeria, Zio Enrico, Reliability, IEEE Transactions on . 2015,第4期

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4. State-parameter dependency estimation of stochastic time series using data transformation and parameterization by support vector regression [C] . Alegria Elvis Omar Jara, Teixeira Hugo Tanzarella, Bottura Celso Pascoli International Conference on Informatics in Control, Automation and Robotics . 2015

机译：通过支持向量回归使用数据转换和参数化的随机时间序列的状态参数依赖性估计
5. Stochastic time series prediction comparison with Markov and support vector machine models. [D] . VanRooy, Theodore. 2006

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State-parameter dependency estimation of stochastic time series using data transformation and parameterization by support vector regression

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