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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Expected Value

Expected Value

The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:

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Related Experiment Video

Updated: Nov 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predictions of bitcoin prices through machine learning based frameworks.

Luisanna Cocco¹, Roberto Tonelli¹, Michele Marchesi¹

¹Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy.

Peerj. Computer Science

|April 9, 2021

Summary

This summary is machine-generated.

High volatility in Bitcoin trading can lead to significant profits. This study found that a single-stage Bayesian Neural Network framework offers the best performance for predicting daily closing Bitcoin prices.

Keywords:

Bayesian neural network Cryptocurrencies Machine learning Technical indicators

Related Experiment Videos

Last Updated: Nov 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Financial Markets
Computational Finance
Machine Learning

Background:

Asset volatility is often viewed negatively, but it presents opportunities for profitable short-term trading, especially in cryptocurrencies like Bitcoin.
The profitability of cryptocurrency trading has increased due to high market volatility.
Predicting Bitcoin's daily closing price is crucial for optimizing trading strategies.

Purpose of the Study:

To compare the performance of various machine learning frameworks for predicting the daily closing Bitcoin price.
To identify the most effective framework through rigorous model selection using k-fold cross-validation.
To evaluate both single-stage and two-stage prediction frameworks.

Main Methods:

Implemented and evaluated single-stage frameworks: Bayesian Neural Network (BNN), Feed Forward Neural Network (FFNN), and Long Short-Term Memory (LSTM) Neural Network.
Developed and assessed two-stage frameworks, cascading BNN, FFNN, or LSTM with Support Vector Regression (SVR).
Utilized k-fold cross-validation for robust model selection and performance evaluation.

Main Results:

Two-stage frameworks generally outperformed their single-stage counterparts, except when BNN was involved.
The single-stage Bayesian Neural Network framework demonstrated the highest predictive performance.
The Mean Absolute Percentage Error (MAPE) for the BNN framework aligns with values reported in existing literature.

Conclusions:

The Bayesian Neural Network, as a single-stage framework, is the most effective model for predicting daily closing Bitcoin prices.
While ensemble methods show promise, the BNN's standalone capability in this context is superior.
The findings provide valuable insights for cryptocurrency traders and researchers focusing on price prediction accuracy.