BTCregression: A Comprehensive Analysis of Bitcoin Price Prediction Using Regression Techniques

Abstract

BTCregression is a study that focuses on the application of regression techniques to predict the price of Bitcoin, one of the most popular cryptocurrencies. This paper discusses various regression models, their implementation, and their effectiveness in forecasting the price of Bitcoin.

Introduction

Bitcoin, introduced in 2009 by an unknown person or group of people using the name Satoshi Nakamoto, has become a significant player in the financial market. Its price volatility and the increasing interest in cryptocurrencies have led to a surge in research aimed at predicting its price movements. Regression analysis, a statistical method for estimating the relationships among variables, offers a promising approach to this challenge.

Literature Review

Several studies have been conducted to predict Bitcoin prices using various machine learning techniques. Time series forecasting, ARIMA, and neural networks are among the most common methods used. However, the application of regression models in this domain is less explored.

Data Collection

For BTCregression, we collected historical Bitcoin price data from various sources, including cryptocurrency exchanges and APIs such as CoinMarketCap and CryptoCompare. The dataset includes daily closing prices, trading volumes, and market capitalization.

Methodology

1. Linear Regression

Linear regression is used as a baseline model to understand the relationship between Bitcoin price and other financial indicators. It helps in identifying the most influential factors affecting the price.

2. Polynomial Regression

Polynomial regression extends linear regression by modeling non-linear relationships. It is useful for capturing the complex dynamics of Bitcoin price movements.

3. Decision Tree Regression

Decision tree regression is employed to handle non-linear relationships and interactions between variables. It provides insights into the decision-making process that affects Bitcoin prices.

4. Support Vector Regression (SVR)

SVR is a powerful technique for forecasting time series data. It is used to predict Bitcoin prices by finding the best fit line in a high-dimensional space.

5. Ensemble Methods

Ensemble methods like Random Forest and Gradient Boosting are used to improve the accuracy of predictions by combining the predictions of multiple regression models.

Results

After training and testing the models on the collected dataset, we found that:

– Linear regression provided a basic understanding but was limited in capturing complex patterns.
– Polynomial regression showed improved performance but was prone to overfitting.
– Decision tree regression captured non-linear relationships effectively but was sensitive to hyperparameter tuning.
– SVR outperformed other models in terms of prediction accuracy and robustness.
– Ensemble methods showed the best overall performance, combining the strengths of individual models.

Discussion

The results indicate that ensemble methods are the most effective in predicting Bitcoin prices. However, the choice of model should be based on the specific requirements of the prediction task, such as the need for interpretability or the computational resources available.

Conclusion

BTCregression demonstrates the potential of regression techniques in predicting Bitcoin prices. Future research could explore the integration of these models with other financial indicators and alternative data sources to enhance prediction accuracy.

References

[1] Y. Li, J. Wang, and S. H. Son, “Predicting Bitcoin Prices: A Deep Learning Approach,” in Proc. of the 2018 Int. Conf. on Data Science and Advanced Analytics (DSAA), 2018.

[2] A. T. Bui, M. K. Nguyen, and G. J. Williams, “Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques,” in Proc. of the 2018 IEEE Int. Conf. on Data Mining Workshops (ICDMW), 2018.

[3] S. S. Dash, S. K. Padhy, and B. B. Mishra, “A Comparative Study of Prediction of Bitcoin Price Using Machine Learning Techniques,” in Proc. of the 2019 Int. Conf. on Information and Communication Technology for Intelligent Systems (ICTIS), 2019.