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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.3K
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

318
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...
318
Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.3K
Associative Learning01:27

Associative Learning

350
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
350
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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一个高效的实时股票预测,利用增量学习和深度学习.

Tinku Singh1, Riya Kalra1, Suryanshi Mishra2

  • 1Department of IT, Indian Institute of Information Technology Allahabad, Prayagraj, U.P. India.

Evolving systems
|April 16, 2024
PubMed
概括
此摘要是机器生成的。

离线-在线学习模型提供比增量学习模型更准确的日内股票预测. 这些模型不断适应实时市场数据,以提高预测准确度.

关键词:
增量学习是一种增量学习.在一天内进行交易.实时预测 实时预测技术指标 技术指标 技术指标

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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相关实验视频

Last Updated: Jun 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Deep Neural Networks for Image-Based Dietary Assessment
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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科学领域:

  • * 金融预测和算法交易.
  • * 机器学习在定量金融中的应用.

背景情况:

  • *日内股票交易依赖于短期价格波动,需要实时预测.
  • * 股票市场的复杂性,波动性和非静止性对准确的预测构成重大挑战.
  • * 传统的机器学习模型需要对当前数据进行超参数调整,以获得最佳性能.

研究的目的:

  • * 提出和评估新的机器学习策略,用于实时的日内股票价格预测.
  • * 为了比较增量学习与线下-线上学习的有效性,用于实时市场预测.
  • * 评估单变量和多变量时间序列数据上的模型性能.

主要方法:

  • * 实现增量学习:通过实时数据流不断更新模型.
  • * 实现线下-线上学习:每次交易会后定期重新培训模型.
  • *适用于单变量 (历史价格) 和多变量 (价格 + 技术指标) 时间序列数据.
  • *对NASDAQ和NSE的八个流动股进行测试.

主要成果:

  • * 与增量学习模型相比,离线-在线学习模型表现出更高的性能.
  • *在采用离线-在线方法的模型中,预测误差明显较低.
  • *这两种方法都应用于单变量和多变量时间序列数据,结果各不相同.

结论:

  • *离线-在线学习是一种更有效的策略,用于在实时市场准确的日内股票价格预测.
  • * 定期的再培训比持续的增量更新更好地捕捉到市场的复杂性.
  • *这些发现为开发自适应算法交易策略提供了有价值的见解.