Lee Sangyeol, Lee Sangjo
Department of Statistics, Seoul National University, Seoul 08826, Korea.
Entropy (Basel). 2021 Apr 7;23(4):433. doi: 10.3390/e23040433.
This study considers support vector regression (SVR) and twin SVR (TSVR) for the time series of counts, wherein the hyper parameters are tuned using the particle swarm optimization (PSO) method. For prediction, we employ the framework of integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) models. As an application, we consider change point problems, using the cumulative sum (CUSUM) test based on the residuals obtained from the PSO-SVR and PSO-TSVR methods. We conduct Monte Carlo simulation experiments to illustrate the methods' validity with various linear and nonlinear INGARCH models. Subsequently, a real data analysis, with the return times of extreme events constructed based on the daily log-returns of Goldman Sachs stock prices, is conducted to exhibit its scope of application.
本研究考虑将支持向量回归（SVR）和孪生支持向量回归（TSVR）用于计数时间序列，其中使用粒子群优化（PSO）方法对超参数进行调整。对于预测，我们采用整数值广义自回归条件异方差（INGARCH）模型框架。作为一个应用，我们考虑使用基于从PSO-SVR和PSO-TSVR方法获得的残差的累积和（CUSUM）检验来处理变点问题。我们进行蒙特卡罗模拟实验，以说明这些方法在各种线性和非线性INGARCH模型下的有效性。随后，进行了一项实际数据分析，该分析基于高盛股票价格的日对数收益率构建极端事件的重现期，以展示其应用范围。
