Bitcoin is the most trending cryptocurrency which is used worldwide. Nowadays many general people or investors investing on bitcoin. But it becomes great challenge to analyze or predict the bitcoin price. Because of its fluctuations it is very hard to predict the price of the bitcoin. By this time machine learning came into picture with many models to analyze the behavior of bitcoin price by using time series data. These models will give better insights to the people who wants to invest on the bitcoin and they will able to understand about the volatility of bitcoin. We can use many machine learning models for prediction. But accuracy of the model is the deciding factor. We used ARIMA, LSTM and Facebook Prophet models and after the prediction is over, we have designed an ensemble model which merges the different models. And based upon the error rate we have decided the best model.

1.
Saxena
and
A.
Sukumar
, "
Predicting bitcoin price using rnn and Compare its predictability with Arima model
,"
International Journal of Pure and Applied Mathematics
, vol.
119
, no.
17
.,
2018
, pp.
2591
2600
.
2.
M.
Amjad
and
D.
Shah
, "
Trading Bitcoin and Online Time Series Prediction
," in
NIPS 2016 Time Series Workshop
,
2017
.
3.
D.
Garcia
and
F.
Schweitzer
, "
Social signals and algorithmic trading of Bitcoin
,"
Royal Society Open Science
, vol.
2
, no.
9
,
2015
.
4.
R.
Chen
and
M.
Lazur
, "
Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement
,"
Stanford Computer Science
, no.
229
,
2011
5.
A.
Go
,
L.
Huang
and
R.
Bhawani
, "
Twitter Sentiment Classification using Distant Supervision
,"
Stanford Computer Science
,
2009
.
6.
B.
Pang
,
L.
Lee
and
S.
Vaidyanathan
, "
Thumbs up: sentiment classification using machine learning techniques
," in
ACL-02 conference on Empirical methods in natural language processing
,
Philadelphia, PA, USA
,
2002
.
7.
M.
Dixon
,
D.
Klabjan
and
J. H.
Bang
, "
Classification-based financial markets prediction using deep neural networks
," ArXiv,
2017
.
8.
S.
McNally
,
J.
Roche
and
S.
Caton
, "
Predicting the price of Bitcoin using machine learning
," in
26th Euro Micro International Conference on Parallel, Distributed and Network-based Processing (PDP
),
2018
9.
M.
Daniela
and
A.
Butoi
, "
Data mining on Romanian stock market using neural networks for price prediction
,"
Informatica Economical
, vol.
17
, no.
3
,
2013
.
10.
H.
Jang
and
J.
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
.
11.
F. A. d.
Oliveira
,
L. E.
Zarate
,
M. d. A.
Reis
and
C. N.
Nobre
, "
The use of artificial neural networks in the analysis and prediction of stock prices
," in
IEEE International Conference on Systems, Man, and Cybernetics
,
2011
.
12.
T.
Rao
and
S.
Srivastava
, "
Analyzing Stock Market Movements Using Twitter Sentiment Analysis
," in
the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
,
2012
.
13.
F. Andrade
de Oliveira
,
L. Enrique
Zairate
and
M. de Azevedo
Reis
,
C. Ner
iNobre
,
The use of artificial neural networks in the analysis and prediction of stock prices
, in
IEEE International Conference on Systems, Man, and Cybernetics
,
2011
, pp.
2151
2155
.
14.
Farokhmanesh
,
F.
,
&Sadeghi
,
M. T.
(
2019
).
Deep Feature Selection using an Enhanced Sparse Group Lasso Algorithm
.
2019 27th Iranian Conference on Electrical Engineering (ICEE
).
15.
Huang
JZ
,
Huang
W.
,
Ni
J.
(
2018
)
Predicting Bitcoin returns using high-dimensional technical indicators
.
J Finance Data Sci
5
(
3
):
140
155
.
This content is only available via PDF.
You do not currently have access to this content.