# Bitcoin Price Predictive Modeling Using Expert Correction

arXiv: https://arxiv.org/abs/2201.02729 · HTML: https://mlsanity.com/paper/2201.02729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Bayesian regression and Bitcoin](https://mlsanity.com/paper/1410.1231/) (2014)

## Built on by (most cited first, 2 of 2)

- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)

## Abstract (arXiv)

The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.