论文标题
高斯流程的多个财务系列插补
Gaussian process imputation of multiple financial series
论文作者
论文摘要
在财务信号处理中，由于依赖潜在市场状态，因此，多个时间序列，例如财务指标，股票价格和汇率汇率强烈，因此必须共同分析它们。我们专注于通过具有表达协方差函数的多输出高斯流程（MOGP）对财务时间序列之间的关系进行学习。在财务系列中学习这些市场依赖性对于财务观察的推出和预测至关重要。在两个现实世界的财务数据集上对所提出的模型进行了实验验证，并分析了它们的跨渠道的相关性。我们将模型与其他MOGP和实际财务数据的独立高斯流程进行了比较。
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process (MOGP) with expressive covariance functions. Learning these market dependencies among financial series is crucial for the imputation and prediction of financial observations. The proposed model is validated experimentally on two real-world financial datasets for which their correlations across channels are analysed. We compare our model against other MOGPs and the independent Gaussian process on real financial data.
