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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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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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相关实验视频

Updated: Jun 5, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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基于自适应学习算法的价格预测模型用于拍卖拍卖-基于深度集群的间隔报价.

Da Ke1, Xianhua Fan2, Muhammad Asif3

  • 1School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的KF-LSTM模型用于拍卖项目价格预测. 该模型在波动期间准确预测价格,达到90%以上的准确性,并提高间隔价格预测可靠性.

关键词:
适应式学习算法适应式学习算法双重集群是指双重集群.在FCM算法中,使用的是FCM算法.时间间隔价格预测预测.这是LSTM的LSTM.

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科学领域:

  • 机器学习 机器学习
  • 计量经济学 计量经济学
  • 数据科学数据科学数据科学

背景情况:

  • 拍卖项目定价由于其复杂的价格特征而带来了挑战.
  • 准确的间隔价格预测对于市场稳定和知情竞价至关重要.

研究的目的:

  • 为准确的拍卖项目间隔价格预测开发一种基于自适应性学习的模型.
  • 为解决拍卖数据集中混价格类别的问题.

主要方法:

  • 一个动态的跨类远程自适应学习模型被用来识别和集群令人困惑的价格类.
  • 使用了一个集群深度算法,集成动态时间曲线 (DTW) 和模糊的C-平均值 (FCM).
  • 构建了一个KF-LSTM模型,该模型结合了长期短期记忆 (LSTM) 和双重集群.

主要成果:

  • 该KF-LSTM模型实现了90.23%的平均准确率和5.41%的平均绝对百分比误差 (MAPE).
  • 该模型显示,实际拍卖价格的间隔覆盖率超过85%,在各种置信级别下.
  • 观察到预测准确度的显著改善,特别是在价格波动期间.

结论:

  • 拟议的KF-LSTM模型为拍卖项目间隔价格预测提供了稳定而准确的解决方案.
  • 这项研究为推进拍卖价格预测方法提供了宝贵的参考.