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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. 
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Probability in Statistics01:14

Probability in Statistics

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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

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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.
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Probability Distributions01:32

Probability Distributions

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 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...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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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...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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...
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Updated: Jun 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Introducing ProsperNN-a Python package for forecasting with neural networks.

Nico Beck1, Julia Schemm1, Claudia Ehrig1

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

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

The prosper_nn package offers four PyTorch neural network models for time series forecasting. It includes novel recurrent neural network sensitivity analysis and uncertainty visualization, now publicly available for research and industry applications.

Keywords:
Financial forecastingMacroeconomic forecastingPrice forecastingRecurrent neural networksSoftware

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • Time series forecasting is crucial for demand and macroeconomic predictions.
  • Existing recurrent neural network (RNN) analysis tools lack accessibility and comprehensive features.
  • Previous uncertainty visualization methods were limited to specific platforms.

Purpose of the Study:

  • Introduce the prosper_nn Python package for advanced time series forecasting.
  • Provide accessible implementations of proven neural network models and novel analytical tools.
  • Facilitate benchmarking and further development of forecasting techniques.

Main Methods:

  • Implementation of four distinct neural network architectures in PyTorch.
  • Development of the first sensitivity analysis method tailored for RNNs.
  • Integration of a heatmap for visualizing forecasting uncertainty.

Main Results:

  • Public release of the prosper_nn package on GitHub.
  • Availability of industry-tested models and methods for broader research use.
  • Enabling new research avenues in time series analysis and forecasting.

Conclusions:

  • The prosper_nn package democratizes access to advanced forecasting tools.
  • It empowers researchers and practitioners to enhance time series prediction accuracy and reliability.
  • Facilitates wider adoption of sophisticated forecasting methods in various domains.