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Updated: Jun 28, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

通过长期短期记忆网络预测的单变股价信号的信心区间的构建

Shankhajyoti De¹, Arabin Kumar Dey¹, Deepak Kumar Gouda¹

¹Department of Mathematics, IIT Guwahati, Guwahati, India.

Annals of data science

|April 16, 2024

概括

这项研究引入了一种新的启动信任区间,用于来自单变长短期记忆 (LSTM) 模型的信号. 它提供了选择最佳区块长度的方法和指导,并使用股票价格数据比较不同的引导策略.

科学领域:

* 时间序列分析.
* 机器学习 * 机器学习
* 统计推理 * 统计推理

背景情况:

*从时间序列数据中准确估计信号对于财务预测至关重要.
*传统的置信区间可能不适合依赖时间序列数据.
* 长短期内存 (LSTM) 网络对于模拟顺序数据是有效的.

研究的目的:

* 开发一种创新的方法来构建使用单变量LSTM模型估计的信号的启动置信区间.
* 评估和比较不同适合依赖数据的引导方法.
* 提供在启动时间序列中选择最佳块长度的指导方针.

主要方法:

* 针对依赖数据结构量身定制的三种不同的引导方法的应用.
* 开发一个基准来比较各种启动策略的表现.
*使用股票价格数据集进行经验验证.

主要成果:

*成功构建了LSTM估计信号的引导置信区间.
* 证明拟议方法在处理依赖时间序列时的有效性.
* 对启动财务数据的最佳区块长度选择的洞察.

结论:

* 提议的引导式方法提供了一种可靠的方法来量化基于LSTM的信号估计中的不确定性.
* 该基准有助于明智地选择适当的启动策略来进行时间序列分析.
*这些发现为提高金融时间序列建模的稳定性提供了实际指导.

关键词:

在 Bootstrap 中使用 Bootstrap.置信区间的时间间隔.预测预测预测预测这是LSTM的LSTM.

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