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相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Expected Value01:15

Expected Value

7.2K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
7.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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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...
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Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Prediction Intervals01:03

Prediction Intervals

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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.
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. 
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Econometric Views (EViews)01:29

Econometric Views (EViews)

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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...
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相关实验视频

Updated: May 6, 2026

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

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关于在经济数据预测中具有强大的基于预测的损失函数的循环神经网络模型.

Wisnowan Hendy Saputra1, Rinda Nariswari2, Matthew Owen2

  • 1Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11530, Indonesia.

MethodsX
|December 1, 2025
PubMed
概括

本研究介绍了基于预测的循环神经网络 (E-RNN),以改善非静止数据的时间序列预测. 与标准的循环神经网络 (RNN) 相比,E-RNN提供了更强大的基于场景的预测.

关键词:
经济预测 经济预测预测性 预测性 预测性 预测性有门的经常性单位.短期长期记忆 短期长期记忆经常性的神经网络.

相关实验视频

Last Updated: May 6, 2026

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

5.8K

科学领域:

  • 机器学习 机器学习
  • 计量经济学 计量经济学
  • 时间序列分析时间序列分析

背景情况:

  • 循环神经网络 (RNN),包括LSTM和GRU,是连续数据的标准,但与非静止和异质时间序列作斗争.
  • 它们的限制源于对称损失函数 (例如,MSE) 假设数据均性.
  • 这阻碍了对各种数据模式和条件的准确预测.

研究的目的:

  • 提出一种新的基于预测的循环神经网络 (E-RNN) 框架,将预测回归整合到RNN中.
  • 开发和比较E-LSTM和E-GRU变体,以进行高级时间序列预测.
  • 通过调整一个不对称参数 (τ) 来实现基于场景的预测 (从悲观到乐观).

主要方法:

  • 开发出基于的循环神经网络 (E-RNN) 变体:E-LSTM和E-GRU.
  • 利用一个不对称的最小平方损失函数来建模超出中心趋势的条件数据分布.
  • 实施基于预测的通用近似交叉验证 (EGACV) 进行稳健的模型选择.

主要成果:

  • 在预测印尼季度经济增长方面,E-RNN模型表现出卓越的表现.
  • 与标准RNN相比,获得了较低的EGACV分数和更高的预测准确性.
  • 在波动性季度到季度 (qtq) 数据上显示出显著的改进,提高了预测可靠性.

结论:

  • 电子RNN提供适应性预测模型,适应数据分布的变化,克服同质性假设.
  • EGACV标准提供了一种可靠的方法来平衡模型适合性和复杂性.
  • 该框架允许通过调整不对称参数 (τ) 来生成各种预测场景.