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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

2.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
2.9K
Confidence Intervals01:21

Confidence Intervals

6.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.0K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.5K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.5K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

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相关实验视频

Updated: May 9, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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基于LSTM符合预测的比特币预测方法,以提高可靠性.

Xiangyue Zhang1, Yuyun Kang2, Chao Li3

  • 1School of Information Science and Engineering, Linyi University, Linyi, Shandong, China.

PloS one
|May 2, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种具有符合性预测 (CP) 模型的长短期记忆 (LSTM),以提高比特币价值预测可靠性. 结合LSTM-CP方法提高了预测准确性,并为加密货币预测提供可验证的置信区间.

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科学领域:

  • 金融技术 金融技术
  • 计算金融是指计算金融.
  • 机器学习 机器学习

背景情况:

  • 加密货币是一种新型资产类别,由于金融技术的进步,它提供了重要的研究机会.
  • 比特币是领先的加密货币,具有相当大的研究价值,但其波动性需要可靠的价值预测.
  • 准确可靠地预测比特币的价值在金融市场上越来越重要.

研究的目的:

  • 开发和评估一种用于提高比特币价值预测可靠性的新方法.
  • 将长期短期记忆 (LSTM) 网络与符合性预测 (CP) 技术相结合,以提高预测准确度.
  • 为比特币价格预测生成可验证的信心区间.

主要方法:

  • 使用斯皮尔曼相关系数方法选择特征,不包括0.75以下和0.95以上的特征.
  • 开发和培训长短期记忆 (LSTM) 模型用于比特币价值预测.
  • 将LSTM预测集成到一个具有量子损失和平均覆盖区间 (ACI) 预测器的合规预测框架中.

主要成果:

  • 拟议的LSTM-符合预测 (LSTM-CP) 模型在预测比特币价值方面表现出更好的可靠性.
  • 由符合性预测模型生成的置信区间验证了LSTM预测的可靠性.
  • 平均覆盖区间 (ACI) 预测器有助于提高预测结果的准确性.

结论:

  • 结合LSTM和符合性预测,为可靠的加密货币价值预测提供了一个强大的方法.
  • LSTM-CP模型有效地解决了比特币价格预测中的波动性挑战.
  • 这项研究为寻求可靠的加密货币市场洞察力的金融分析师和研究人员提供了宝贵的工具.