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

4.4K
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...
4.4K
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Econometric Views (EViews)01:29

Econometric Views (EViews)

145
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
145
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

105
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
105

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

Cervical cancer burden and elimination readiness in the US-affiliated Pacific Islands: A structured narrative review.

Cancer epidemiology·2026
Same author

Genome-wide identification of the GIF gene family in Zanthoxylum armatum and functional characterization of ZaGIF5 in plant growth and drought tolerance.

Plant science : an international journal of experimental plant biology·2026
Same author

Subcutaneous adipose tissue outperforms muscle strength as an indicator of survival and quality of life in patients with cancer: a multicenter cohort study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

The Clinical Efficacy and Safety of Efgartigimod in the Treatment of Autoimmune Encephalitis.

Immunological investigations·2026
Same author

A nanoscale robotic cleaner.

Nature communications·2026
Same author

Declines in cervical cancer incidence among young women in the United States.

Journal of the National Cancer Institute·2026
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

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

10.7K

强大的多维时间序列预测.

Chen Shen1, Yong He1, Jin Qin1

  • 1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.

Entropy (Basel, Switzerland)
|January 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种强大的时间非负矩阵因子化预测模型 (RTNMFFM),用于处理杂的高维时间序列数据. 新的框架提高了预测的准确性和稳定性,特别是在缺失或异常值的情况下.

关键词:
在L2,1规范中,L2,1是正常的.多维时间序列预测.非负矩阵因子化 (NMF)强壮的 坚固的 坚固的

更多相关视频

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

相关实验视频

Last Updated: Jul 4, 2025

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

10.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

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:

  • 大规模,高维的时间序列数据在智能交通和环境监测中普遍存在.
  • 数据异常,包括噪音,异常值和缺失值,对准确的预测构成重大挑战.
  • 传统的去除或替换异常数据的方法可能会导致有价值的信息丢失.

研究的目的:

  • 开发一种新的多维时间序列预测框架,能够有效处理异常数据.
  • 在数据不完善的情况下,提高时间序列预测模型的稳定性和预测准确性.

主要方法:

  • 为多维时间序列提出了一个强大的时间非负矩阵因子化预测模型 (RTNMFFM).
  • 将自回归调节器和L2,1规范集成到非负矩阵因子化 (NMF) 中,以提高稳定性和减少过.
  • 引入周期性平滑处罚,以提高具有显著缺失值的数据的预测准确性.
  • 使用交替梯度下降算法进行模型训练.

主要成果:

  • 与标准时间序列预测方法相比,RTNMFFM表现出优越的稳定性.
  • 拟议的模型实现了明显更好的预测准确性,特别是在具有严重缺失值的数据集上.
  • 实验结果验证了集成自回归调节器,L2.1规范和周期性平滑惩罚的有效性.

结论:

  • RTNMFFM为预测具有异常的复杂,高维时间序列数据提供了有效的解决方案.
  • 该模型处理噪声,异常值和缺失值的能力使其适合于现实应用,如智能运输和环境监测.
  • 拟议的框架代表了可靠的时间序列预测的重大进步.