Bitcoin Price Prediction using SVM and ARIMA
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ISSN (Online) 2581-9429
IJARSCT
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
Volume 6, Issue 2, June 2021
Impact Factor: 4.819
Bitcoin Price Prediction using SVM and ARIMA
Model
Gausiya Momin1, Trupti Ingle2, Vaishnavi Mirajkar3, A. A. Magar4
Students, Department of Computer Engineering1,2,3
Professor, Department of Computer Engineering4
Sinhgad Academy of Engineering, Pune, India
Abstract: Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin
have highly fluctuated which makes them very difficult to predict. This research aims to discover the
most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using
one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to
January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had
experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and
the R-Square (R2) was as high as 99.2 Percentage.
Keywords: Bitcoin; Cryptocurrency; Machine Learning
I. INTRODUCTION
Time series prediction is not a new phenomenon. Prediction of mature financial markets such as the Stock market
has been researched at length. Bitcoin presents a fascinating time series prediction problem in a market still in its short-
lived stage. As a result, there is a high irregularity in the market and this provides an opportunity in terms of prediction
with adoption growing consistently over time due to the open nature of bitcoin. To decrease the risks, this project has
been carried out to predict the price of bitcoin using recurrent neural network (RNN), support vector machine (SVM),
and linear regression (LR) to predict the price of bitcoin. Evaluation of these algorithms is carried out to determine.
II. PREDICTION TECHNIQUES
2.1 Linear Regression Model
Linear regression is one of the most significantly used predictive modelling techniques. In this model, the
relationship between a dependent variable and independent variables. Linear regression is used to fit a predictive model
to an observed data set of values of the response and explanatory variables.
2.2 Support Vector Machine
Support Vector Machines (SVM) are popularly and widely used for classification problems in machine learning.
There are a few important parameters of SVM that you should be aware of before proceeding further: Kernel, Hyper
plane, Decision Boundary.
2.3 ARIMA Model
ARIMA stands for AutoRegressive Integrated Moving Average. It is a class of models that captures a suite of
different standard temporal structures in time series data. A popular used statistical method for time series forecasting
in this model.
III. LITERATURE SURVEY
[1] In recent years, Bitcoin is the most valuable in the cryptocurrency market. However, prices of Bitcoin have
highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient
and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute
Copyright to IJARSCT DOI: 10.48175/IJARSCT-1486 1094
www.ijarsct.co.in
ISSN (Online) 2581
2581-9429
IJARSCT
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
Volume 6, Issue 2, June 2021
Impact Factor: 4.819
interval trading data on the Bitcoin exchange website named bitstamp from Januaryy 1, 2012 to January 8, 2018, some
different regression models with scikit learn and Keras libraries had experimented.
experimented. The best results showed that the
Mean Squared Error (MSE) was as low as 0.00002
0. and the R-Square (R2) was as high as 99.2 percentage.
percentage
[2] Crypto-currency
currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied
to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first
handles, it is identified
ied the price trend on day by day changes in the Bitcoin price while it gives knowledge about
Bitcoin price trends. The dataset till current dateis taken with open, high, low and close price details of Bitcoin value.
Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to
derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy.
Experiment results are compared for decision tree and regression m model.
IV. PROPOSED WORK
It is important to be allowed to predict Bitcoin price changes. The stock market prediction has grown over decades
using daily data and accessible high-frequency
frequency data. As we studied previously we predicted Bitcoin price in two ways:
empirical
pirical analysis and analysis of robust machine learning algorithms. Machine learning algorithms has been widely
used to make accurate predictions in many areas, including product manufacturing and finance. There are more
methods about feature selection and d measurements are an advantage, previous related works have depended on the
researchers’ domain knowledge and lack a comprehensive consideration of feature dimensions.
dimensions
Figure 4.1: Block diagram
V. CONCLUSION
Machine learning techniques have recently gained a lot of popularity among the international community. The main
purpose of this dissertation was to know whether these new approaches are more powerful than the traditional methods,
or not. The results show that SVM predictions have a better display on average an improvement of 92% and 94%,
respectively, for the ARIMA model.
Copyright to IJARSCT DOI: 10.48175/IJARSCT-1486 1095
www.ijarsct.co.in
ISSN (Online) 2581-9429
IJARSCT
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
Volume 6, Issue 2, June 2021
Impact Factor: 4.819
REFERENCES
[1] D. Shah and K. Zhang, “Bayesian regression and Bitcoin,” in 52nd Annual Allerton Conference on
Communication, Control, and Computing (Allerton),2015, pp. 409-415.
[2] Huisu Jang and Jaewook Lee, “An Empirical Study on Modelling and Prediction of Bitcoin Prices with
Bayesian Neural Networks based on Blockchain Information,” in IEEE Early Access Articles, 2017, vol. 99,
pp. 1-1.
[3] M. Daniela and A. BUTOI, “Data mining on Romanian stock market using neural networks for price
prediction”.informatica Economica, 17,2013.
[4] Jui-Sheng Chou and Thi-Kha Nguyen "Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-
Optimized Machine-Learning Regression" in IEEE Transactions on industrial
informatics,2018,vol.14,pp.1551-3203.
[5] Ruchi Mittal, Shefali Arora and M.P.S Bhatia "Automated cryptocurrencies prices prediction using machine
learning"in Division of Computer Engineering, Netaji Subhas Institute of Technology,
India,2018,vol.8,pp.2229-6956.
Copyright to IJARSCT DOI: 10.48175/IJARSCT-1486 1096
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