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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. 
2.2K
Probability in Statistics01:14

Probability in Statistics

12.4K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Histograms01:17

Probability Histograms

11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.1K
Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K

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相关实验视频

Updated: Jun 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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介绍ProsperNN-一个Python软件包,用于用神经网络进行预测.

Nico Beck1, Julia Schemm1, Claudia Ehrig1

  • 1Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Bavaria, Germany.

PeerJ. Computer science
|December 9, 2024
PubMed
概括

该prosper_nn包提供了四个PyTorch神经网络模型用于时间序列预测. 它包括新的循环神经网络灵敏度分析和不确定性可视化,现在可以公开用于研究和工业应用.

关键词:
金融预测 财务预测宏观经济预测 宏观经济预测预测价格 预测价格 预测价格经常性的神经网络.软件 软件 软件 软件 软件

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Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
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Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
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科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 时间序列预测对于需求和宏观经济预测至关重要.
  • 现有的循环神经网络 (RNN) 分析工具缺乏可访问性和全面功能.
  • 以前的不确定性可视化方法仅限于特定平台.

研究的目的:

  • 介绍了prosper_nn Python包,用于高级时间序列预测.
  • 提供经过验证的神经网络模型和新型分析工具的可访问的实现.
  • 促进基准测试和预测技术的进一步发展.

主要方法:

  • 在PyTorch中实现四个不同的神经网络架构.
  • 开发首个针对RNN量身定制的灵敏度分析方法.
  • 整合热图以可视化预测不确定性.

主要成果:

  • 在GitHub上公开发布prosper_nn包.
  • 业界测试的模型和方法的可用性,用于更广泛的研究.
  • 在时间序列分析和预测方面开辟新的研究途径.

结论:

  • 繁荣包民主化了对先进预测工具的访问.
  • 它使研究人员和从业人员能够提高时间序列预测的准确性和可靠性.
  • 促进各种领域更广泛地采用复杂的预测方法.