Jove
Visualize
联系我们

相关概念视频

Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

616
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
616
Exponential and Sinusoidal Signals01:18

Exponential and Sinusoidal Signals

267
The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
267
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
Exponential Fourier series01:24

Exponential Fourier series

208
In audio signal processing, the exponential Fourier series plays a crucial role in sound synthesis, allowing complex sounds to be broken down into simpler sinusoidal components. This decomposition process is fundamental in analyzing and reconstructing musical notes and other audio signals. The exponential Fourier series expresses periodic signals as the sum of complex exponentials at both positive and negative harmonic frequencies, providing a powerful tool for signal analysis.
Euler's identity...
208
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

441
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
441

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

Mean Reversion and Heavy Tails: Characterizing Time-Series Data Using Ornstein-Uhlenbeck Processes and Machine Learning.

Sensors (Basel, Switzerland)·2026
Same author

Autocatalytic Sets and Assembly Theory: A Toy Model Perspective.

Entropy (Basel, Switzerland)·2024
Same author

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors.

Animals : an open access journal from MDPI·2023
Same author

On the Applicability of Quantum Machine Learning.

Entropy (Basel, Switzerland)·2023
Same author

Combining Fractional Derivatives and Machine Learning: A Review.

Entropy (Basel, Switzerland)·2023
Same author

Interpolating Strange Attractors via Fractional Brownian Bridges.

Entropy (Basel, Switzerland)·2022
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

相关实验视频

Updated: Jul 7, 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

缩放时间序列数据的指数:一种机器学习方法.

Sebastian Raubitzek1,2, Luiza Corpaci3, Rebecca Hofer2

  • 1Information and Software Engineering Group, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, Austria.

Entropy (Basel, Switzerland)
|December 23, 2023
PubMed
概括

机器学习模型准确地估计了赫斯特指数,在时间序列数据上表现优于传统方法,如重新缩放范围 (R/S) 分析和确定波动分析 (DFA). 这种新的方法增强了金融等领域的长期依赖性分析.

关键词:
赫斯特的指数是一个指数.人工智能的人工智能是人工智能.复杂性的复杂性 复杂性的复杂性机器学习是机器学习.回归分析是一种回归分析.扩展指数是一个扩展指数.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
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

33.7K

相关实验视频

Last Updated: Jul 7, 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
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
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

33.7K

科学领域:

  • 时间序列分析时间序列分析
  • 统计建模 统计建模
  • 机器学习应用 机器学习应用

背景情况:

  • 赫斯特指数量化了时间序列数据中的远程依赖.
  • 传统的方法,如重新缩放范围 (R/S) 分析和确定波动分析 (DFA) 具有局限性,特别是在微分的莱维运动中.
  • 现有的方法通常需要复杂的预处理步骤,如功率频谱计算.

研究的目的:

  • 开发一种基于机器学习的新方法,用于准确的赫斯特指数估计.
  • 解决传统方法在区分分数列维运动和分数布朗运动方面的局限性.
  • 从时间序列数据直接对缩放指数进行连续估计.

主要方法:

  • 在已知Hurst指数的合成数据上训练机器学习模型 (LightGBM,MLP,AdaBoost).
  • 利用分数布朗运动和分数列维运动来生成数据.
  • 从时间序列直接估计缩放指数,没有功率光谱分析.

主要成果:

  • 机器学习估计器显著超过传统的R/S分析和DFA.
  • 拟议的方法显示出卓越的准确性,特别是对于类似于分数莱维运动的数据.
  • 对财务数据的验证揭示了与文献的差异,但证实了该方法与已知的基本事实相对准确.

结论:

  • 机器学习为Hurst指数估计提供了一个强大而准确的替代方案.
  • 这种方法通过将机器学习与传统金融方法相结合,推进时间序列分析.
  • 这些发现为分析复杂的时间序列数据以更高的精度开辟了新的途径.