Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

229
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.
For potentiometric titration, the Gran plot is created by plotting...
229
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

178
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
178
Survival Tree01:19

Survival Tree

49
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
49
Scatter Plot01:15

Scatter Plot

6.7K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Behavioral and Biochemical Evaluation of a Curcumin-Loaded Nano-Liposomal Formulation in a Scopolamine-Induced Mouse Model of Cognitive Impairment.

Biomolecules & therapeutics·2026
Same author

Correction: Kim et al. Identification of <i>GREM-1</i> and <i>GAS6</i> as Specific Biomarkers for Cancer-Associated Fibroblasts Derived from Patients with Non-Small-Cell Lung Cancer. <i>Cancers</i> 2025, <i>17</i>, 2858.

Cancers·2026
Same author

Anbalcabtagene autoleucel (PD-1 and TIGIT knockdown CD19 CAR-T) for relapsed/refractory large B-cell lymphoma (CRC01-01).

Blood·2026
Same author

Fibrinogen concentrates versus cryoprecipitate for intraoperative hypofibrinogenemia in liver transplantation: study protocol for a randomized trial (FIBCRYO-LT trial).

Trials·2026
Same author

Analysis of Tuberculosis Case Incidence and Trends in 2025.

Public health weekly report·2026
Same author

Seroprevalence of Japanese Encephalitis Virus, and Immunogenicity of the Vero-Cell Culture-Derived Vaccine in Hematopoietic Stem Cell Transplantation Recipients.

Journal of Korean medical science·2026

相关实验视频

Updated: May 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

多变量时间序列模式预测的动态周期性事件图.

SoYoung Park1, HyeWon Lee1, Sungsu Lim1

  • 1Department of Computer Science and Engineering, Chungnam National University, Daejeon, Republic of South Korea.

PeerJ. Computer science
|March 10, 2025
PubMed
概括

本研究引入了动态周期事件图 (PEGs),通过纳入数据周期性来改进多变量时间序列分析. 通过捕捉反复出现的模式,PEGs提高了链接预测的准确性,优于现有的方法.

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 时间序列分析时间序列分析

背景情况:

  • 复杂的系统需要强大的多变量时间序列模式分析.
  • 动态图形神经网络是有效的,但往往忽视数据周期性.
  • 忽视周期性会降低时间序列中的预测准确性.

研究的目的:

  • 为时间序列分析引入动态周期事件图 (PEGs).
  • 通过利用固有的数据周期性来提高预测准确性.
  • 改进多变量时间序列中的链接预测.

主要方法:

  • 时间序列分解以提取季节性组件并确定代表性时期.
  • 频率分析以确定季节性组件中的关键周期.
  • 提取图案模式 (反复出现的子序列) 来定义事件节点.
  • 基于周期性图案模式构建动态的双重事件图.

主要成果:

  • 在链接预测性能中显示了超过5%的改进.
  • 在传导式和归纳式学习场景中实现了更高的准确性.
  • 在多个周期性多变量时间序列数据集上验证的有效性.
关键词:
动态图表的动态图表事件图表事件图表.图形神经网络是一个神经网络.链接预测链接预测多变量时间序列分析.自主监督学习学习

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K

相关实验视频

Last Updated: May 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K

结论:

  • 动态PEG有效地捕获和利用时间序列数据中的周期性.
  • 该方法在预测准确性和概括性方面提供了实质性的改进.
  • 公开可用的代码确保了可复制性,并促进了未来的研究.