Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
Cryptocurrency Price Prediction with Neural Networks of
LSTM and Bayesian Optimization
Ehsan Sadeghi Pour, Hossein Jafari, Ali Lashgari, Elaheh Rabiee, and Amin Ahmadisharaf
ABSTRACT
In this paper we present a price prediction for Bitcoin prices. The
Submitted : February 07, 2022
methodology used is a hybrid artificial neural network model of Long Short-
Term Memory and Bayesian Optimization. This is a complex model with a Published : March 07, 2022
high prediction power, which to our knowledge has not been applied to ISSN: 2507-1076
prediction of cryptocurrency prices to date. Following Charandabi and
Kamyar (2021), we elaborate on previous methods used for prediction of DOI: 10.24018/ejbmr.2022.7.2.1307
cryptocurrency prices and build on their methodology. We conclude with
detailed graphs and tables of optimization results.
E. S. Pour
Department of Electrical and Computer
Keywords: Bayesian optimization, artificial neural networks, cryptocurrency Engineering, Islamic Azad University,
price prediction, long short-term memory. Kashan, Iran
(e-mail: Ehsan61.ai@gmail.com)
H. Jafari
Master of Accounting, Allameh Amini
Institute of Higher Education,
Mazandaran, Iran.
(email: hossein.jafari2008@gmail.com)
A. Lashgari*
Department of Economics, Kansas State
University, Manhattan, KS, USA.
(e-mail: alilashgari@ksu.edu)
E. Rabiee
Department of Economics, Kansas State
University, Manhattan, KS, USA.
(e-mail: elahehrabiee@ksu.edu)
A. Ahmadisharaf
Kansas State University, Manhattan, KS,
USA.
(e-mail: ahmadish@ksu.edu)
*Corresponding Author
combination with one another to improve accuracy and
I. INTRODUCTION predictive power. Charandabi and Kamyar (2021B) present a
survey on the literature on applications of artificial neural
The world has gone through many changes since 2009. Just
networks to cryptocurrency price prediction. Based on their
over the last 13 years Instagram was founded, Steve Jobs
reports, there have been a number of hybrid and singular
passed away, a whole pandemic started, and so on. But an
artificial neural network models implemented so far;
extremely important event that happened in 2009 hand has
however, there are still numerous gaps to be filled in the
been changing the world since is the launch of Bitcoin. While
literature.
the cryptocurrency was conceptualized back in 1990’s, it was
This paper serves to fill the literature by providing a model
not relevant to the public until years after Bitcoin was first
of Neural Networks of LSTM and Bayesian Optimization. In
implemented. The rate of increase in popularity is so high that
Section II, we review related literature; in Section III, we
Bitcoins which barely sufficed to by a pizza in 2010, costed
discuss the data process, in Section IV we go through
as high as $68000 in 2021.
optimization details, and in section V we conclude.
Financial analysts attribute the popularity of Bitcoin to its
decentralization, secure blockchain system, and control over
value. Charandabi and Kamyar (2021A) presents a thorough
II. LITERATURE REVIEW
survey of the history, foundation, and relevance of Bitcoin. In
the same paper, they argue on the importance and relevance “Artificial neural networks (ANNs) are biologically
of artificial neural networks to predict prices of inspired computational networks. Multilayer perceptrons
cryptocurrency. Artificial neural networks may have a high (MLPs), the ANNs most commonly used for a wide variety
predictive power and can be pertained to cryptocurrency price of problems, are based on a supervised procedure and
data in any time horizon. comprise three layers: input, hidden, and output.” (Park &
Artificial neural network models can be used in Lek, 2016) In this context, artificial neural networks may be
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 20RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
powerful tools to predict growing or volatile numerical view the data aspect of this research paper as a novel work in
values. the literature. All exchange rates are with respect to United
Within the financial literature, artificial neural networks States Dollars.
have been applied to prediction of stocks market indices since
decades ago. Singular networks often have a lower predictive
power than hybrid models, though they take shorter to IV. OPTIMIZATION
compute. (Ghashami et al., 2021) Here we present a series of The baseline treatment is prediction of Bitcoin prices with
price prediction for Bitcoin prices. The methodology used is Neural Networks of LSTM and Bayesian Optimization. The
a hybrid artificial neural network model of Long Short-Term value of optimal delays is set to 1.30, and the value of optimal
Memory and Bayesian Optimization. This complex model training percentage (division of data to training and testing)
has a high predictive power over prediction of numerical is set to 0.8 (Nejatian, 2022). Furthermore, to optimize speed,
values. To our knowledge, it has not been applied to the the optimal code execution environment is the CPU. The
context of prediction of cryptocurrency prices to date optimal drop out value is 0.5. These values are endogenous to
“Long Short-Term Memory (LSTM) is a specific recurrent the model and remain constant across the treatments.
neural network (RNN) architecture that was designed to Some general deep learning parameters are exogenously
model temporal sequences and their long-range dependencies varied across treatments. In the baseline, the maximum
more accurately than conventional RNNs.” (Sak et al., 2014) number of training Epochs in deep learning algorithms
As described in the survey of Charandabi & Kamyar (2021B), (maxEpoch) is set equal to 400. Following standard
LSTM is frequently a candidate for hybrid artificial neural cryptocurrency optimization literature (Charandabi &
network models. Another optimization method, Bayesian Kamyar, 2021C) we exogenously set it to 200 in the treatment
Optimization, is a sequential design strategy for global run. Additionally, in the baseline, the minimum batch size of
optimization of black-box (unknown internal structure) training Epochs in deep learning algorithms is set equal to 32.
functions (Mockus 2012). The novel hybrid structure of Following standard cryptocurrency optimization literature
LSTM and Bayesian Optimization has been tested on stock (Charandabi & Kamyar, 2021C) we exogenously set it to 16
market indices (Huang et al., 2018) and showed a high in the treatment run.
predictive power, therefore, we view it as suitable for We start reporting results by the baseline of Bitcoin prices
prediction of cryptocurrency indicators. (weekly from January 2020 through January 2022),
As elaborated on in Charandabi & Kamyar (2021C), the maximum training Epoch number of 400, and batch size of
high volatility of cryptocurrency prices roots from maximal training Epochs in deep learning algorithms of 32. Fig. 1
trading, that stems from uncertainty. Decision-making under depicts the input data, reporting the values for mean and
under unforeseen events has been the focus of research in the standard deviation as well. The algorithm normalizes input
last few decades (Asadi et al., 2022). More recently, data prior to running, in order for the base numbers to be
(Dehghani Filabadi 2019), Filabadi (2022) proposed general feasible for computational concerns. Fig. 2 depicts the
mathematical models for addressing uncertainties in normalized input data, reporting the values for mean and
environments where data is sensitive to prediction errors. standard deviation as well. In the green and blue lines, Fig. 3
This approach was further extended for other applications depicts the minimum observed objective and estimated
such as energy management (Filabadi & Azad, 2020), minimum objective, respectively.
network management (Filabadi & Bagheri, 2021), and
inventory and healthcare (Filabadi & Mahmoudzadeh, 2022).
III. DATA
We extracted data from Yahoo! Finance, a major resource
for daily data on financial indices and cryptocurrency prices.
Ghashami et al. (2021) elaborates on data gathering for
financial indicators for the purposes of time-series prediction
through artificial neural network methodology. We follow the
same system for optimization of data points among the daily
values for Open, High, Low, Close, and Adj. Close.
Cryptocurrency data is extremely volatile by nature.
There’s a body of literature dedicated to prediction of
volatility of cryptocurrency price data, as explained
thoroughly in the survey paper Charandabi & Kamyar
(2021C). In order to avoid running into problems and
Fig. 1. Input data of first treatment.
retrieving weak predictions, we use weekly data, and expand
the time horizon instead. The employed data runs from
January 10, 2020, to January 10, 2022, on a weekly basis.
Most of the literature on prediction of cryptocurrency prices
employ data from a short time horizon, as explained in the
survey paper Charandabi & Kamyar (2021B). Therefore, we
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 21RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
Fig. 2. Normalized input data of first treatment. Fig. 5. Error mean evaluation.
Fig. 3. Minimum observed objective and estimated minimum objective
values.
Fig. 6. Error histogram.
Fig. 4-7 depict technical aspects of training data. Fig. 4
shows the output evaluation and reports the rank correlation
number, which is significantly high. Fig. 5 shows the error
evaluation and reports the MSE (Mean Squared Error),
RMSE (Rooted Mean Square Error), and NRMSE
(Normalized Rooted Mean Square Error). Figure 6 shows the
error histogram, reporting Error Mean and Error Standard
Deviation numbers. Fig. 7 shows the Regression Graph
Evaluation of train data, reporting the R-squared value, that
is highly significant.
Fig. 7. Regression graph evaluation.
The output results had Maximum Objective Evaluations of
60 reached. Total time elapsed was 849.8 seconds, with a total
function evaluation count of 60 and a total objective function
evaluation time of 766.6 seconds. Observed objective
function value was 0.07662, with an estimated objective
function value of 0.15971 and a function evaluation time of
7.8227. Also, estimated objective function value was 0.14654
and estimated function evaluation time was 6.7355. Fig. 8- 10
represent technical aspects of the test data. Fig. 8 depicts
Fig. 4. Output evaluation.
output evaluation, and reports rank correlation value. Fig. 9
shows the error evaluation and reports the MSE (Mean
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 22RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
Squared Error), RMSE (Rooted Mean Square Error), and MSE (Mean Squared Error), RMSE (Rooted Mean Square
NRMSE (Normalized Rooted Mean Square Error). Fig. 10 Error), and NRMSE (Normalized Rooted Mean Square Error)
shows the error histogram, reporting Error Mean and Error of all data. Fig. 12 depicts regression evaluation of all data,
Standard Deviation numbers. reporting the R-squared value.
Fig. 8. Output evaluation of test data.
Fig. 11. Error mean evaluation.
Fig. 9. Error mean evaluation of test data.
Fig. 12. Regression graph evaluation.
Table I shows technical details of the best observed
feasible point (above) and the best estimated feasible point
(below), according to the models. All points are on the first
layer.
TABLE I: BEST FEASIBLE POINTS
LSTM Initial Layer 2
Number of Units
Layer Learn Rate Reg.
68 2 0.016 0.00475
71 2 0.021 0.00798
The treatment run employed the same methodology and
data, with the exception of the maximum number of training
Epochs in deep learning algorithms (maxEpoch) set equal to
200, and the minimum batch size of training Epochs in deep
Fig. 10. Error histogram of test data. learning algorithms set equal to 16. In the green and blue
lines, Fig. 13 depicts the minimum observed objective and
Fig. 11 and 12 show technical details of all (i.e., train and estimated minimum objective, respectively.
test) data. Fig. 11 depicts the error evaluation and reports the
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 23RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
Fig. 13. Minimum observed objective and estimated minimum objective
values.
Fig. 16. Error histogram.
Fig. 14-17 depict technical aspects of training data. Fig. 14
shows the output evaluation and reports the rank correlation
number, which is significantly high. Fig. 15 shows the error
evaluation and reports the MSE (Mean Squared Error),
RMSE (Rooted Mean Square Error), and NRMSE
(Normalized Rooted Mean Square Error). Fig. 16 shows the
error histogram, reporting Error Mean and Error Standard
Deviation numbers. Fig. 17 shows the Regression Graph
Evaluation of train data, reporting the R-squared value, that
is highly significant.
Fig. 17. Regression graph evaluation.
The output results had Maximum Objective Evaluations of
60 reached. Total time elapsed was 849.8 seconds, with a total
function evaluation count of 60 and a total objective function
evaluation time of 1271.6 seconds. Observed objective
function value was 0.08878, with an estimated objective
function value of 0.13354 and a function evaluation time of
18.863. Also, estimated objective function value was
0.071925 and estimated function evaluation time was 9.4717.
Fig. 14. Output evaluation. Fig. 18-20 represent technical aspects of the test data. Fig. 18
depicts output evaluation, and reports rank correlation value.
Fig. 19 shows the error evaluation and reports the MSE
(Mean Squared Error), RMSE (Rooted Mean Square Error),
and NRMSE (Normalized Rooted Mean Square Error). Fig.
20 shows the error histogram, reporting Error Mean and Error
Standard Deviation numbers.
Fig. 21 and 22 show technical details of all (i.e., train and
test) data. Fig. 21 depicts the error evaluation and reports the
MSE (Mean Squared Error), RMSE (Rooted Mean Square
Error), and NRMSE (Normalized Rooted Mean Square Error)
of all data. Fig. 22 depicts regression evaluation of all data,
reporting the R-squared value.
Table II shows technical details of the best observed
feasible point (above) and the best estimated feasible point
(below), according to the models.
Fig. 15. Error mean evaluation.
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 24RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
TABLE II: BEST FEASIBLE POINTS
Number of Number of LSTM Initial Layer 2
Layer Units Layer Learn Rate Reg.
3 61 1 0.021 0.00881
1 54 2 0.012 0.00889
While the 200-16 model provides less noise in the resulting
data and filters volatilities, the 400-32 model yields a shorter
elapsed time and higher accuracy factors.
Fig. 21. Error mean evaluation.
Fig. 18. Output evaluation of test data.
Fig. 22. Regression graph evaluation.
V. CONCLUSION
In this paper, we applied a novel algorithm for prediction
of time-series data (through hybrid artificial neural networks
of Long Short-Term Memory and Bayesian Optimization) to
prediction of Bitcoin data. We ran two treatments of data and
training variables using Bitcoin prices (weekly from January
2020 through January 2022): maximum training Epoch
Fig. 19. Error mean evaluation of test data. number of 400 and 200, and batch size of training Epochs in
deep learning algorithms of 32 and 16. Results were reported
graphically and in tables, and optimal solutions were
comparatively shown.
There exist many possible extensions to this paper. One
may run the same algorithm with different data to compare
consistency of external validity to other contexts. Other
bitcoin prices (e.g., Ethereum) may be good candidates.
Alternatively, within the financial data realm, stock market
indices could be tested with the same algorithm. Furthermore,
other hybrid models such as a hybrid of the current model
with a GA-ANFIS artificial neural network algorithm a la
Ghashami & Kamyar (2021) could be implemented to
increase the accuracy. In light of the current impact and
relevance of cryptocurrency prices, it’s essential that further
models be tested.
Fig. 20. Error histogram of test data.
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 25RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
REFERENCES Javadi, S., Maghami, A. & Hosseini, S. M. (2021) A deep learning approach
based on a data-driven tool for classification and prediction of
Amidi, Y., Nazari, B., Sadri, S., & Yousefi, A. (2021). Parameter Estimation thermoelastic wave’s band structures for phononic crystals, Mechanics
in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point of Advanced Materials and Structures, DOI:
Process State-Space Modeling Framework. Neural Computation, 10.1080/15376494.2021.1983088.
33(5), 1269-1299. Kamyar, K. (2019) “Effect of Unemployment Length on Employment
Andersen, T. G., Bollerslev, T., Diebold, F., Labys, P.: Modeling and Expectations”. Undergraduate Economic Review.
forecasting realized volatility. Econometrica 71, 579–625 (2003). Kavousi-Fard, A., Mohammadi, M., Al-Sumaiti, A.S. (2021) “Effective
Charandabi, S. E., & Kamyar, K. (2021) “Using A Feed Forward Neural strategies of flexibility in modern distribution systems: reconfiguration,
Network Algorithm to Predict Prices of Multiple Cryptocurrencies”. renewable sources and plug-in electric vehicles. Flexibility in Electric
European Journal of Business and Management Research, 6(5), 15-19. Power Distribution Network”s, pp. 95–119. CRC Press, Boca Raton,
https://doi.org/10.24018/ejbmr.2021.6.5.1056. FL.
Charandabi, S. E., & Kamyar, K. (2021). Prediction of Cryptocurrency Price Keshishian, M., Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D.,
Index Using Artificial Neural Networks: A Survey of the Literature. & Mesgarani, N. (2020). Estimating and interpreting nonlinear
European Journal of Business and Management Research, 6(6), 17-20. receptive field of sensory neural responses with deep neural network
https://doi.org/10.24018/ejbmr.2021.6.6.1138. models. Elife, 9, e53445.
Charandabi, S. E., & Kamyar, K. (2021) “Survey of Cryptocurrency Kharazmi, O., & Jahangard, S. (2020). A new family of lifetime distributions
Volatility Prediction Literature Using Artificial Neural Networks,” in terms of cumulative hazard rate function. Communications Faculty
of Sciences University of Ankara Series A1 Mathematics and Statistics,
Business and Economic Research, Macrothink Institute. 12 (1).
69(1), 1-22.
https://doi.org/10.5296/ber.v12i1.19301.
Kharazmi, O., Saadatinik, A., & Jahangard, S. (2019). Odd hyperbolic cosine
Charandabi, S., & Ghanadiof, O. (2022). Evaluation of Online Markets
exponential-exponential (OHC-EE) distribution. Annals of Data
Considering Trust and Resilience: A Framework for Predicting Science, 6(4), 765-785.
Customer Behavior in E-Commerce. Journal of Business and Lei, M., and Mohammadi, M. (2021) Hybrid machine learning based energy
Management Studies, 4(1), 23–33. policy and management in the renewable-based microgrids considering
https://doi.org/10.32996/jbms.2022.4.1.4. hybrid electric vehicle charging demand. Int. J. Electr. Power Energy
Charandabi, S. E., Ghashami, F., & Kamyar, K. (2021) “US-China Tariff Syst., 128, 106702.
War: A Gravity Approach,” Business and Economic Research, Madanizadeh, S. A., and Setayesh, A. "Macroeconomic Uncertainty and
Macrothink Institute. 11 (3). https://doi.org/10.5296/ber.v11i3.18757. Economic Development." (2020). Working Paper.
Charandabi, S. E. (2020). Prediction of Customer Churn in Banking Industry. Maghami, A. & Hosseini, S. M. (2021) Intelligent step-length adjustment for
Age 18(92). adaptive path-following in nonlinear structural mechanics based on
Chavoshi, S.F.; Mashayekhi, M. (2015) Estimating Demand Function of the group method of data handling neural network, Mechanics of Advanced
Fixed Line Telecommunication in Iran. Asian J. Res. Soc. Sci. Humanit. Materials and Structures, DOI: 10.1080/15376494.2021.1880677.
5, 275. Mahmoudi, M. (2022). COVID Lessons: Was there any way to reduce the
Cheng, T.; Zhu, X.; Gu, X.; Yang, F.; Mohammadi, M. (2021) Stochastic negative effect of COVID-19 on the United States economy? arXiv
energy management and scheduling of microgrids in correlated preprint arXiv:2201.00274.
environment: A deep learning-oriented approach. Sustain. Cities Soc. Mahmoudi, M., & Ghaneei, H. (2022). Detection of structural regimes and
69, 102856. analyzing the impact of crude oil market on Canadian stock market:
Dehghani Filabadi, M. (2019). Robust optimization for SCED in AC-HVDC Markov regime-switching approach. Studies in Economics and
power systems (Master's thesis, University of Waterloo). Finance.
Dehghani Filabadi, M., & Bagheri, P. (2021). Robust-and-Cheap Framework Mehrara, M., & Behradmehr, N., & Ahrari, M., & Mohaghegh, M. (2010).
for Network Resilience: A Novel Mixed-Integer Formulation and Forecasting volatility of crude oil price using the GMDH neural
Solution Method. arXiv e-prints, arXiv-2110. network. Energy Economics Review, 7(25), 89-112.
Filabadi, M.D, Asadi, A.., Giahi, R., Ardakani, A.T., Azadeh, A. (2022). A Miremadi, A., Kenar Roudi, J., & Ghanadiof, O. (2021). Evaluation on Role
New Stochastic Model for Bus Rapid Transit Scheduling with of Electronic Word of Mouth (EWOM) Ads in Customers’ Emotions
Uncertainty, Future Transportation, 1(2). and Choices in E-Shops. International Journal of Industrial Marketing,
Filabadi, M. D., & Mahmoudzadeh, H. (2022). Effective Budget of 6(1). https://doi.org/10.5296/ijim.v6i1.
Uncertainty for Classes of Robust Optimization. INFORMS Journal on Miremadi, A., & Ghanadiof, O. (2021). CRM Competitive Strategy in
Optimization (in press). Financial Institutions. European Journal of Business and Management
Filabadi, M.D., (2022). A New Paradigm in Addressing Data Uncertainty: Research, 6(3), 111-117. https://doi.org/10.24018/ejbmr.2021.6.3.867.
Discussion and Future Research, Academia Letters, Article 4775. Miremadi, A., Golchobian, M. M. A., & Ghanadiof, O. (2021). Requirement
https://doi.org/10.20935/AL4775. and Architecture of Organization Development. European Journal of
Filabadi, M. D., & Azad, S. P. (2020). Robust optimisation framework for Business and Management Research, 6(4), 55-64.
SCED problem in mixed AC-HVDC power systems with wind https://doi.org/10.24018/ejbmr.2021.6.4.932.
uncertainty. IET Renewable Power Generation, 14(14), 2563-2572. Mobtahej, M., Esapour, K., Tajalli, S.Z., Mohammadi, M. (2021) Effective
Fardhosseini, M. S., Soltaninejad, M., Karji, A., Ghorbani, Z., & Ghanadiof, demand response and GANs for optimal constraint unit commitment in
O. (2021). Qualitative Evaluation of 5S Application Considering the solar-tidal based microgrids. IET Renew. Power Gener. 1–11
Experience of Electrical Construction Experts. https://doi.org/10.1049/rpg2.12331.
Ghanadiof, O. (2021). Customer Loyalty and Powerful Brand in Heavy Mockus, J. (2012). Bayesian approach to global optimization: theory and
Machinery Industry. European Journal of Business and Management applications. Kluwer Academic.
Research, 6(3), 195-199. https://doi.org/10.24018/ejbmr.2021.6.3.903. Mohaghegh, M., & A. S. Valipour, (2020). Income-dependent impacts of
Ghashami, F., & Kamyar, K. (2021) “Performance Evaluation of ANFIS and financial development and human capital on economic growth. A non-
GA-ANFIS for Predicting Stock Market Indices”. International stationary panel analysis. Theoretical and Applied Economics.
Journal of Economics and Finance. Mohaghegh, M., and A. S. Valipour. "Triggering Economic Growth: Trade
https://doi.org/10.5539/ijef.v13n7p1. Liberalization as the Prominent Factor in Less-developed Countries."
Ghashami, F., Kamyar, K., & Riazi, S. A. (2021) “Prediction of Stock Market Business and Economic Research 11.2 (2021): 252-265.
Index Using a Hybrid Technique of Artificial Neural Networks and Mohammadi, M., Kavousi-Fard, A., Dabbaghjamanesh, M., Farughian, A.,
Particle Swarm Optimization”. Applied Economics and Finance, 8, 1. & Khosravi, A. (2021). Effective Management of Energy Internet in
https://doi.org/10.11114/aef.v8i3.5195. Renewable Hybrid Microgrids: A Secured Data Driven Resilient
Ghanadiof, O., Miremadi, A., & Mohammadian, M. (2021). Strategic Architecture. IEEE Transactions on Industrial Informatics.
Planning and Strategic Analysis of Food Industry Using SWOT. Nakamoto, S. (2008) “Bitcoin: A Peer-to-Peer Electronic Cash System.”
Scientific Research Journals in Management and Social Studies, 2(23), https://bitcoin.org/bitcoin.pdf.
14–33. Nazari, F. and Yan, W. Convolutional versus Dense Neural Networks:
Huang, B., Ding, Q., Sun, G., & Li, H. (2018). Stock prediction based on Comparing the Two Neural Networks’ Performance in Predicting
Bayesian-LSTM. Proceedings of the 2018 10th International Building Operational Energy Use Based on the Building Shape arXiv
Conference on Machine Learning and Computing. preprint arXiv:2108.12929.
https://doi.org/10.1145/3195106.3195170. Nazemi, A.; Shaghaghi, V.; Mashayekhi, M. (2014) Market Power
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 26RESEARCH ARTICLE
European Journal of Business and Management Research
www.ejbmr.org
Assessment of Iran’s Wholesale Electricity Market. Asian J. Res.
Mark., 3, 172–177.
Nazemi, A.; Mashayekhi, M. Modelling Welfare Loss in Iranian Electricity
Market. (2014) In Proceedings of the 37th IAEE International
Conference, Energy and the Economy, New York, USA, June 2014
Nejatian, A. (2022). Time Series Prediction with Bayesian optimization,
MATLAB Central File Exchange.
Nisar, M. U., Voghoei, S., & Ramaswamy, L. (2017, June). Caching for
pattern matching queries in time evolving graphs: challenges and
approaches. In 2017 IEEE 37th International Conference on
Distributed Computing Systems (ICDCS) (pp. 2352-2357). IEEE.
Park, Y.S., & Lek, S. (2016). Artificial Neural Networks. Developments in
Environmental Modelling, 123–140. Springer.
https://doi.org/10.1016/b978-0-444-63623-2.00007-4.
Pourbemany, J., Zhu, Y., & Bettati, R. (2021). A Survey of Wearable
Devices Pairing Based on Biometric Signals. arXivpreprint
arXiv:2107.11685.
Pourbemany, J., Mirjalily, G., Abouei, J., & Raouf, A. H. F. (2018, May).
Load Balanced Ad-Hoc On-Demand Routing Basedon Weighted Mean
Queue Length Metric. In Electrical Engineering (ICEE), Iranian
Conference on (pp. 470-475). IEEE.
Sak, H., Senior, A. W., and Beaufays, F. (2014). Long short-term memory
recurrent neural network architectures for large scale acoustic
modeling. In Interspeech, Google Research.
Ulstad, A., Mashayekhi, M. and Mechstroth, H. Work in Progress: Do
Engineering Students Gain Financial Literacy Skills by Taking an
Engineering Economy Course? (2018) American Society of
Engineering Education (ASEE), Paper ID# 21471
Yahoo! (2022). Bitcoin USD (BTC-USD) price history & historical data.
Yahoo! Finance. Retrieved January 15, 2022, from
https://finance.yahoo.com/quote/BTC-USD/history/.
Yousefi, A., Amidi, Y., Nazari, B., & Eden, U. T. (2020). Assessing
Goodness-of-Fit in Marked Point Process Models of Neural Population
Coding via Time and Rate Rescaling. Neural Computation, 32(11),
2145-2186.
DOI: http://dx.doi.org/10.24018/ejbmr.2022.7.2.1307 Vol 7 | Issue 2 | March 2022 27You can also read
