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相关概念视频

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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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

351
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...
351
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

85
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
85
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

563
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
563
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
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...
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相关实验视频

Updated: Jul 13, 2025

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

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时间序列的合规预测.

Chen Xu, Yao Xie

    IEEE transactions on pattern analysis and machine intelligence
    |October 11, 2023
    PubMed
    概括

    我们开发了一种新的方法,可以在不需要数据假设的情况下创建可靠的时间序列预测间隔. 我们的EnbPI算法为序列数据提供了高效,可扩展和准确的预测.

    科学领域:

    • 时间序列分析时间序列分析
    • 统计预测 统计预测
    • 机器学习 机器学习

    背景情况:

    • 准确的预测间隔对于时间序列预测至关重要.
    • 现有的方法通常依赖于强烈的分布假设或数据分割.
    • 可扩展性和计算效率是序列预测中的关键挑战.

    研究的目的:

    • 为时间序列的无分布预测间隔引入一个一般框架.
    • 确定预测间隔的覆盖范围和大小的理论界限.
    • 为构建这些间隔提供一个高效和可扩展的算法.

    主要方法:

    • 开发了一个关于无分布预测间隔的一般框架.
    • 建立了对覆盖差距和设置差异的明确界限.
    • 介绍了集成批量预测间隔 (EnbPI) 算法.
    • EnbPI使用集体预测器,与合规预测相关,但不需要可交换性.

    主要成果:

    • 拟议的框架为预测间隔的准确性提供了明确的界限.
    • 在某些假设下,EnbPI提供异常消失的覆盖范围和尺寸差距.
    • 在计算上,EnbPI是高效的,避免了数据的分割,并且可以用于顺序预测.

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  • 广泛的模拟和实时数据分析证实了EnbPI的有效性.

    • 结论:

    • EnbPI提供了一个强大的和高效的方法来构建没有分布的预测间隔.
    • 该方法适用于连续时间序列预测,其中数据交换性不保证.
    • EnbPI为现有的预测间隔技术提供了一个可扩展和计算优势的替代方案.