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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.4K
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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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
405
Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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相关实验视频

Updated: Jul 12, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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基于注意力的动态图表神经网络用于资产定价.

Ajim Uddin1, Xinyuan Tao1, Dantong Yu1

  • 1Martin Tuchman School of Management, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, USA.

Global finance journal
|November 1, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于资产定价的新图形神经网络模型,通过分析公司网络和市场动态,有效预测股票回报. 该模型提高了投资组合的表现,并捕捉了关键的市场事件.

关键词:
资产定价是指资产的定价.在C3333中,它是C33.在C52中,我们使用了C52.C63 C63 的意思是金融科技公司 (Fintech)金融网络 金融网络G1010 一个人的生活G1414 一个人的生活图表卷积神经网络的图.机器学习 机器学习神经网络的神经网络的神经网络

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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相关实验视频

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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科学领域:

  • 量化金融 量化金融
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 公司网络显著影响资产定价.
  • 现有的模型往往忽略了动态的公司间关系.

研究的目的:

  • 为资产定价开发一种新的图形神经网络 (GNN) 模型.
  • 整合动态网络结构和固定的信息,以改善预测.
  • 评估模型在回报预测和投资组合优化方面的有效性.

主要方法:

  • 利用图表注意力机制来学习动态股票市场网络结构.
  • 在学习网络中使用循环卷积神经网络 (CNN) 进行信息扩散.
  • 结合图形神经网络与循环和卷积组件,用于端到端的资产定价.

主要成果:

  • 拟议的GNN模型有效地预测了股票收益率.
  • 在投资组合业绩中观察到显著的改善.
  • 该模型在各种测试和模拟数据中表现出强大而持久的性能.
  • 学习的动态网络准确地反映了主要的市场事件.

结论:

  • 新型GNN模型成功地捕捉了市场网络结构和动态移动.
  • 这种方法为投资者和监管机构提供了宝贵的见解.
  • 该模型为理解和预测复杂市场环境中的资产价格提供了一个强大的工具.