论文标题
高斯过程的神经推断，用于类星体的时间序列数据
Neural Inference of Gaussian Processes for Time Series Data of Quasars
论文作者
论文摘要
类星体光曲线的研究提出了两个问题：推理功率谱和不规则采样时间序列的插值。这些任务的基线方法是用阻尼随机步行（DRW）模型插值时间序列，其中使用最大似然估计（MLE）推断频谱。但是，DRW模型并未描述时间序列的平滑度，而MLE在优化和数值精度方面面临许多问题。在这项工作中，我们介绍了一种新的随机模型，我们称之为$ \ textit {卷曲潮湿随机步行} $（cdrw）。该模型向DRW引入了平滑度的概念，使其能够完全描述Quasar Spectra。我们还介绍了一种新的高斯过程参数推理方法，我们称之为$ \ textit {neural推断} $。该方法使用最新神经网络的力量来改善常规的MLE推理技术。在我们的实验中，神经推理方法可显着改善基线MLE（RMSE：0.318 \ rightarrow 0.205 $，$ 0.464 \ rightarrow 0.444 $）。此外，CDRW模型和神经推断的组合在插值典型的类星体光曲线方面显着超过了基线DRW和MLE（$χ^2 $：$ 0.333 \ rightarrow 0.998 $，$ 2.695 \ rightarrow 0.981 $）。该代码在GitHub上发布。
The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE). However, the DRW model does not describe the smoothness of the time series, and MLE faces many problems in terms of optimization and numerical precision. In this work, we introduce a new stochastic model that we call $\textit{Convolved Damped Random Walk}$ (CDRW). This model introduces a concept of smoothness to a DRW, which enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $\textit{Neural Inference}$. This method uses the powers of state-of-the-art neural networks to improve the conventional MLE inference technique. In our experiments, the Neural Inference method results in significant improvement over the baseline MLE (RMSE: $0.318 \rightarrow 0.205$, $0.464 \rightarrow 0.444$). Moreover, the combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE in interpolating a typical quasar light curve ($χ^2$: $0.333 \rightarrow 0.998$, $2.695 \rightarrow 0.981$). The code is published on GitHub.
