System for Analysis and Prediction of Trends in Cryptocurrency Market
DOI:
https://doi.org/10.4108/eai.10-3-2022.173608Keywords:
Blockchain, Bitcoin, Time Series, Forecasting, Regression, Machine Learning, Neural Networks, Crypto currencyAbstract
In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM.
Downloads
References
J. Grossman and R. J. Shiller(1980), The Determinants of the Variability of Stock Market Prices, National Bureau of Economic Research, Working Paper 564.
Giuliano Armano, Michele Marches,Andrea Murru (2005), Hybrid genetic-neural architecture for stock indexes forecasting Information Sciences 170(1):3-33
Erol Egrioglu ,Cagdas Hakan Aladag,Ufuk Yolcu,(2013), Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks, Expert Systems with Applications 40(3):854–857.
Kazem, A., Sharifi, E., Hussain, F.K., Saberi, M., & Hussain, O.K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput., 13, 947-958.
S. McNally, J. Roche and S. Caton, "Predicting the Price of Bitcoin Using Machine Learning," 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2018, pp. 339-343, doi: 10.1109/PDP2018.2018.00060
Alvin Ho1, Ramesh Vatambeti, Sathish Kumar Ravichandran1 Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model, Indian Journal of Science and Technology, Year: 2021, Volume: 14, Issue: 27, Pages: 2300-2308, DOI 10.17485/IJST/v14i27.878
Mrs Vaidehi M , Alivia Pandit et al., Bitcoin price prediction using machine learning. International Journal of Engineering Technologies and Management Research, 8(5), 20. doi: 10.29121/ijetmr.v8.i5.2021.953
Marco Stocchi Michele Marchesi(2017) Fast wavelet transform assisted predictors of streaming time series, DOI: 10.1016/j.softx.2017.09.006
Miller, Dante, and Jong-Min Kim. 2021. Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies. Journal of Risk and Financial Management 14: 486. https://doi.org/10.3390/ jrfm14100486.
S. Roy, S. Nanjiba and A. Chakrabarty, "Bitcoin Price Forecasting Using Time Series Analysis," 2018 21st International Conference of Computer and Information Technology (ICCIT), 2018, pp. 1-5, doi: 10.1109/ICCITECHN.2018.8631923.
Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo , Predicting the Volatility of Cryptocurrency Time-Series: MAF 2018, Mathematical and Statistical Methods for Actuarial Sciences and Finance
S. Lauren and S. D. Harlili, Stock trend prediction using simple moving average supported by news classification," in Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014 International Conference of. IEEE, 2014, pp. 135-139.
Rathindra Sarathy, Krish Muralidhar,Perturbation Methods for Protecting Numerical Data: Evolution and Evaluation,Editor(s): Ranajit Chakraborty, C.R. Rao, Pranab Sen,Handbook of Statistics, Elsevier, Volume 28, 2012, Pages 513-531,ISSN 0169-7161,ISBN 9780444518750
A. Iwok, A. S. Okpe, A Comparative Study between Univariate and Multivariate Linear Stationary Time Series Models, American Journal of Mathematics and Statistics p-ISSN: 2162-948X.e-ISSN :162-8475,2016;6(5): 203-212, doi:10.5923/j.ajms.20160605.02.
Nicola Uras et al., Forecasting Bitcoin closing price series using linear regression and neural networks models,Uras et al. (2020), PeerJ Comput. Sci., DOI: 10.7717/peerj-cs.279
Uras, N., Marchesi, L., Marchesi, M., & Tonelli, R. (2020). Forecasting Bitcoin closing price series using linear regression and neural networks models. PeerJ. Computer science, 6, e279. https://doi.org/10.7717/peerj-cs.279.
A. A. Ariyo, A. O. Adewumi and C. K. Ayo, "Stock Price Prediction Using the ARIMA Model," 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 2014, pp. 106-112, doi: 10.1109/UKSim.2014.67.
I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques.Morgan Kaufmann, 2005.
F. A. Gers, D. Eck, and J. Schmidhuber, Applying LSTM to time series predictable through time-window approaches," pp. 669{676, 2001
S. Hochreiter and J. Schmidhuber, LSTM can solve hard long time lag problems Advances in neural information processing systems, pp. 473-479, 1997.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
