论文标题
基于模型的和无模型的点预测算法，用于本地固定的随机字段
Model-Based and Model-Free point prediction algorithms for locally stationary random fields
论文作者
论文摘要
无模型预测原理已成功应用于一般回归问题，以及涉及固定和局部时间序列的问题。在本文中，我们演示了如何应用无模型的预测来处理仅本地固定的随机字段，即可以假定它们仅在其整个定义区域的有限零件上固定在有限的零件上。我们使用结合趋势和/或异质性的模型来构建一步预测的预测指标，并比较了无模型与基于模型的预测的性能。本文的两个方面（基于模型和模型）都是在局部（但不是全球）固定的随机字段的背景下进行的。我们演示了基于模型和无模型点预测方法在合成数据以及来自CIFAR-10数据集中的图像中的应用，在后一种情况下，我们的最佳无模型点预测结果优于使用基于模型预测获得的预测。
The Model-free Prediction Principle has been successfully applied to general regression problems, as well as problems involving stationary and locally stationary time series. In this paper we demonstrate how Model-Free Prediction can be applied to handle random fields that are only locally stationary, i.e., they can be assumed to be stationary only across a limited part over their entire region of definition. We construct one-step-ahead point predictors and compare the performance of Model-free to Model-based prediction using models that incorporate a trend and/or heteroscedasticity. Both aspects of the paper, Model-free and Model-based, are novel in the context of random fields that are locally (but not globally) stationary. We demonstrate the application of our Model-based and Model-free point prediction methods to synthetic data as well as images from the CIFAR-10 dataset and in the latter case show that our best Model-free point prediction results outperform those obtained using Model-based prediction.
