论文标题
通过长期短期存储网络预测的单变量股票价格信号的置信区间构建
Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network
论文作者
论文摘要
在本文中，我们展示了一种创新的方法，用于构建基于单变量LSTM模型估计的信号的自举置信区间。我们采用三种不同类型的引导方法进行依赖设置。我们规定了一些有用的建议，以在执行样品的引导时选择最佳块长度。我们还提出了一个基准，以比较通过不同的引导策略测量的置信区间。我们通过某些股价数据集说明了实验结果。
In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.
