Laboratoire de Mathématiques de Besançon, Université de Franche-Comté, UFR des Sciences et Techniques. 16, route de Gray. F-25030 Besançon Cedex, France.
Cegos Deployment. 11 rue Denis Papin. F-25000 Besançon, France.
Neural Netw. 2014 Jul;55:59-71. doi: 10.1016/j.neunet.2014.03.004. Epub 2014 Mar 21.
Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.
储层计算是一种最近提出的机器学习范例，它已经在处理经验数据方面表现出了优异的性能。我们研究了一种特殊类型的储层计算机，称为时滞储层，它是由时滞微分方程解的采样构建而成，并展示了它们在预测多元离散时间非线性随机过程的条件协方差方面的良好性能，这些过程的类型为 VEC-GARCH 型，以及在预测使用日内报价计算的实际每日市场实现波动率方面的良好性能，使用的训练输入是中等规模的日对数收益率序列。我们解决了与单个操作储层缺乏任务通用性相关的一些问题，并提出了一种基于使用时滞储层并行阵列的解决方案。
