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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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Survival Tree01:19

Survival Tree

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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...
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Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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相关实验视频

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Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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基于间歇性特征调整的备件需求预测方法

Lilin Fan1, Xia Liu1, Wentao Mao1

  • 1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括

本研究引入了一种新的转移学习方法,用于预测复杂设备后市场零件的间歇性需求. 该方法通过在不同的需求序列中调整间歇性特征来提高预测准确性和稳定性.

科学领域:

  • 运营研究 运营研究
  • 供应链管理 供应链管理
  • 机器学习 机器学习

背景情况:

  • 对复杂设备售后零件的需求往往是零星和间歇性的.
  • 这种间歇性导致单一需求序列中的信息不足,阻碍了传统的预测方法.
  • 现有的预测技术与间歇性需求数据的独特特征作斗争.

研究的目的:

  • 开发一种有效的预测方法,用于复杂设备售后市场零件中间歇性需求.
  • 解决处理零星需求模式的现有方法的局限性.
  • 提高制造业售后服务需求预测的准确性和稳定性.

主要方法:

  • 建议采用转移学习方法来适应间歇性特征.
  • 使用需求发生和间隔信息开发了一个间歇时间序列域分区算法.
  • 层次聚类用于将序列划分为子源域.
  • 权重向量结合了间歇性和时间性特征,用于跨领域学习.

主要成果:

  • 拟议的方法有效预测复杂设备售后市场零件的未来需求趋势.
  • 在真实世界数据集上的实验结果表明,预测稳定性得到了显著的改善.
  • 与现有方法相比,需求预测的准确性大大提高了.
关键词:
深度学习是一种深度学习.需求预测需要预测.间歇性的时间序列.备件管理 备件管理转移学习转移学习

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

  • 间歇性特征适应方法为零星需求预测提供了一个强大的解决方案.
  • 转移学习有效地利用不同需求序列的信息.
  • 该方法为优化复杂设备行业的库存和维护策略提供了有价值的工具.