The research goal is to study criteria which affect the price of a cryptocurrency and their usage for the price forecasting using Machine Learning techniques. The subject of research is forecasting of most famous cryptocurrency Bitcoin using the most popular Python Data Science Platform - Anaconda. Research Methods are machine learning, data analysis, and multiple regression. A set of criteria which affect the price of the cryptocurrency were defined based on the analysis of public information. Their correctness was tested using Machine Learning algorithms. As a result of simulation experiment through the application using real data from open sources, we have found that selected combination of criterion can explain more than 70% of cryptocurrencies prices variation using either Multiple Regression or Random Trees or Long ShortTerm Memory networks.
The story of cryptocurrencies had started in 2009 when Bitcoin was born. It was the first decentralized digital currency with emphasis on cryptography [1]. After nine years, almost 1500 live cryptocurrencies exist [2], and their number continues to overgrow [3].
Another trending sphere that is developing rapidly in the last few years is Machine Learning (ML). It is a method of data analysis that automates analytical model building. ML allows applications to find hidden insights without being explicitly programmed [4]. One of the most common task ML solves correctly is forecasting.
The purpose of the paper is to formalize criteria which affect the price of cryptocurrencies and use them in the price forecasting experiment developed with Python Anaconda Data Science tool with ML algorithms.
The paper is organized as follows: part 2 describes dataset and ML algorithms used in an experiment and the results of the experiment, and the last part concludes.
Cryptocurrencies Dataset and ML Prediction Algorithms
Information about cryptocurrencies like their prices per date, their supply, mining
difficulty, and other is open source. So we combined data into one dataset [5]. It has
next columns:
• date, from 25th of January 2017 to 2
In our research we decided to use three supervised ML algorithms to verify if these criteria can be used in cryptocurrency’s price prediction in such combination: • Multiple Linear Regression with default configuration sklearn configuration; • Random Forests with 100 trees; • Long Short-Term Memory Networks with 50 neurons on a hidden layer and 100 epochs of training.
Linear regression shows relations between variables and how changes affect them. Random Forests algorithm uses a bagging approach to create a bunch of decision trees with a random subset of data. LSTMs are capable of learning long-term dependencies. The research question is: Which criteria affect the price of the cryptocurrency and how? We have found next points:
2. Mining difficulty level; 3. Cryptocurrency’s trading volume; 4. Perceptions of the cryptocurrency’s value by the society; 5. Price of Bitcoin.
After all three predictions, we were able to find mean squad error and coefficient of determination. Linear regression application output was:
25000 20000 15000 10000 5000 In the graphic below we display the results of both predictions in contrast to real values. (fig. 3). Thus we have tested the correctness of the selected criteria combination on their effect on the price of Bitcoin. For our experiment, we used Multiple Linear Regression, Random Forests, and LSTM ML algorithms implemented with Python in Anaconda Data Science tool. As a result, we have found that selected combination of criterion can explain more than 70% of Bitcoin’s price.
On this base, we plan to study additional criteria which affect prices of
cryptocurrencies to be able to forecast their prices more accurate.
3. 2018 Will See Many More Cryptocurrencies Double In Value,
https://www.forbes.com/sites/kenrapoza/2018/01/0