TL;DR: A forecasting experiment using roughly forty candidate features, feature selection, and five model families to estimate short-horizon Bitcoin price movement.
About This Document
Amine Batsi, Mohamed Biniz, and Rachid El Ayachi assemble market and technical variables, reduce them using correlation and mutual-information measures, and compare linear regression, XGBoost, support-vector regression, Prophet, and a bidirectional gated recurrent unit. They report error and fit metrics including MSE, MAE, MAPE, and R-squared for the selected datasets and prediction horizon.
Why Bitcoin People May Care
Model comparisons can show how methods behaved on one dataset without proving a durable trading edge. Bitcoin regimes change, correlated features can leak information, and low historical error does not remove execution cost or tail risk. This is a 2025 research result, not a live forecast, investment recommendation, or guarantee that any model will perform outside the authors' experiment.
Document Details
• Title: Forecasting Bitcoin Price Fluctuations with Time-Series Models
• Author / organization: Amine Batsi, Mohamed Biniz, and Rachid El Ayachi
• Year: 2025
• Language: English
• Document type: Financial machine-learning research paper
• Pages: 12
Download access
Unlock a direct PDF download of this document. The download is hosted externally. CacheRat provides researched identification and access; purchase does not transfer ownership or expand the document’s existing usage rights.
#pdf #bitcoin #price_forecasting #time_series
About This Document
Amine Batsi, Mohamed Biniz, and Rachid El Ayachi assemble market and technical variables, reduce them using correlation and mutual-information measures, and compare linear regression, XGBoost, support-vector regression, Prophet, and a bidirectional gated recurrent unit. They report error and fit metrics including MSE, MAE, MAPE, and R-squared for the selected datasets and prediction horizon.
Why Bitcoin People May Care
Model comparisons can show how methods behaved on one dataset without proving a durable trading edge. Bitcoin regimes change, correlated features can leak information, and low historical error does not remove execution cost or tail risk. This is a 2025 research result, not a live forecast, investment recommendation, or guarantee that any model will perform outside the authors' experiment.
Document Details
• Title: Forecasting Bitcoin Price Fluctuations with Time-Series Models
• Author / organization: Amine Batsi, Mohamed Biniz, and Rachid El Ayachi
• Year: 2025
• Language: English
• Document type: Financial machine-learning research paper
• Pages: 12
Download access
Unlock a direct PDF download of this document. The download is hosted externally. CacheRat provides researched identification and access; purchase does not transfer ownership or expand the document’s existing usage rights.
#pdf #bitcoin #price_forecasting #time_series
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