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相关概念视频

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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相关实验视频

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一个基于模型的LSTM和图形卷积网络用于股票趋势预测.

Xiangdong Ran1, Zhiguang Shan2, Yukang Fan3

  • 1Beijing Information Technology College, Beijing, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括

本研究介绍了一种使用长短期记忆 (LSTM) 和图形卷积网络的新型模型,通过分析相互依赖来预测股票市场趋势. 该模型显示了预测准确度的提高和投资者有利可图的交易策略.

关键词:
图表 卷积网络 卷积网络长期短期记忆 长期短期记忆股票交易决定 股票交易决定股票趋势预测 股票趋势预测

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科学领域:

  • 量化金融 量化金融
  • 机器学习 机器学习
  • 金融计量经济学 金融计量经济学

背景情况:

  • 股票市场的动态是复杂的,受个人股票之间的相互依赖的影响.
  • 准确的股票趋势预测对于稳定的投资利至关重要.
  • 从数据中识别和建模这些隐藏的依赖关系是一个重大挑战.

研究的目的:

  • 通过有效地捕捉相互依存关系,开发一种先进的股票趋势预测模型.
  • 提高股票价格趋势预测的准确性.
  • 为投资者提供最佳时机和股票交易价格的工具.

主要方法:

  • 利用长短期内存 (LSTM) 网络从库存数据中提取特征.
  • 从LSTM隐藏状态输出构建了图形节点.
  • 使用皮尔森相关系数来建立图形结构.
  • 应用图形卷积网络 (GCN) 用于特征提取和预测.

主要成果:

  • 拟议的LSTM-GCN模型在股票趋势预测准确度方面显著超过了基线方法.
  • 使用该模型进行的交易后期测试在上和下跌的市场上都产生了有利的回报.
  • 根据模型的见解,确定了有效的交易策略.

结论:

  • 集成的LSTM-GCN模型有效地捕捉了复杂的股票相互依赖,以改善趋势预测.
  • 该模型为寻求优化交易决策和提高利能力的投资者提供了宝贵的工具.
  • 在现实世界股票市场场景中证明了实际适用性和利能力 (中国A50).