Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

347
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
347
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

95
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:
95

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Entropy-Augmented Forecasting and Portfolio Construction at the Industry-Group Level: A Causal Machine-Learning Approach Using Gradient-Boosted Decision Trees.

Entropy (Basel, Switzerland)·2026
Same author

Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach.

Entropy (Basel, Switzerland)·2025
Same author

Impact of Dietary Fat and Oral Delivery System on Cannabigerol Pharmacokinetics in Adults.

Cannabis and cannabinoid research·2024
Same author

Sucrose Concentration and Fermentation Temperature Impact the Sensory Characteristics and Liking of Kombucha.

Foods (Basel, Switzerland)·2023
Same author

Breaking water carbon nexus by the natural biological system: ultimate solution for ESG challenges.

Environmental science and pollution research international·2023
Same author

ESG risks and corporate survival.

Environment systems & decisions·2022
Same journal

Scale, trust, and the digital divide: a systematic review of AI and ML for agricultural applications.

Frontiers in artificial intelligence·2026
Same journal

Beyond uncertainty in modern active learning for trustworthy AI.

Frontiers in artificial intelligence·2026
Same journal

Eco-FinOps: a causal-agentic framework for energy-efficient and explainable cloud cost optimization.

Frontiers in artificial intelligence·2026
Same journal

Multimodal graph neural network with large language models for node and link prediction.

Frontiers in artificial intelligence·2026
Same journal

Efficient representation of boolean decision structures through Boolean function optimization.

Frontiers in artificial intelligence·2026
Same journal

Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.

Frontiers in artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: May 13, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

2.5K

使用AI预测比特币的价格.

Gil Cohen1, Avishay Aiche1

  • 1Department of Management, Western Galilee Academic College, Acre, Israel.

Frontiers in artificial intelligence
|May 9, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 显著提升了比特币的投资回报,超过了机器学习 (ML) 和传统策略. 人工智能适应市场变化,展示其在加密货币交易和投资组合管理方面的潜力.

关键词:
在这里,我们可以看到AIAIAI.比特币 比特币 比特币 比特币算法算法是一种算法.加密货币加密货币是什么意思机器学习是机器学习.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

910

相关实验视频

Last Updated: May 13, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

2.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

910

科学领域:

  • 计算金融是指计算金融.
  • 金融领域的人工智能
  • 加密货币市场分析分析

背景情况:

  • 比特币的价格波动给传统的投资策略带来了挑战.
  • 金融市场日益复杂,需要先进的分析工具.
  • 像加密货币这样的新兴资产类别需要新的预测方法和战略开发方法.

研究的目的:

  • 研究人工智能 (AI) 和机器学习 (ML) 在预测比特币价格变动方面的有效性.
  • 开发和评估基于AI和ML的适应性投资策略.
  • 将人工智能驱动的策略与基于机器学习和传统的买入持有 (B&H) 方法的性能进行比较.

主要方法:

  • 从2018年1月到2024年1月的比特币业绩数据的分析.
  • 使用一组神经网络开发一个人工智能驱动的战略.
  • 整合预测分析和技术指标,用于动态调整市场风险.

主要成果:

  • 由人工智能驱动的战略实现了1640.32%的总回报率.
  • 这显著超过了基于ML的方法 (304.77%) 和传统的B&H策略 (223.40%).
  • 人工智能策略在经济衰退期间表现出有效的损失减轻,在有利条件下获得最大化.

结论:

  • 人工智能对金融市场具有变革性的潜力,特别是在波动性加密货币交易中.
  • 与传统方法相比,人工智能驱动的策略提供了卓越的性能和适应性.
  • 先进的AI技术为市场动态提供了更深入的见解,影响了投资组合管理和风险评估.