References
- G. Abdoli, Comparing the prediction accuracy of LSTM and ARIMA models for time-series with permanent fluctuation, Periódico do Núcleo de Estudos e Pesquisas sobre Gênero e DireitovCentro de Ciências Jurídicas-Universidade Federal da Paraíba, vol. 9, 2020. ⇒27710.22478/ufpb.2179-7137.2020v9n2.50782
- F. Abedini, M. Bahaghighat, M. S’hoyan, Wind turbine tower detection using feature descriptors and deep learning, Facta Universitatis, Series: Electronics and Energetics, 33, 1 (2019) 133–153. ⇒267, 28410.2298/FUEE2001133A
- R. Ali, J. Barrdear, R. Clews, J. Southgate, Innovations in payment technologies and the emergence of digital currencies, Bank of England Quarterly Bulletin, Q3, 2014. ⇒266
- E. Amouee, M. M. Zanjireh, M. Bahaghighat, M. Ghorbani, A new anomalous text detection approach using unsupervised methods, Facta universitatis-series: Electronics and Energetics, 33, 4 (2020) 631–653. ⇒26710.2298/FUEE2004631A
- G. S. Atsalakis, I. G. Atsalaki, F. Pasiouras, C. Zopounidis, Bitcoin price forecasting with neuro-fuzzy techniques, European Journal of Operational Research, 276, 2 (2019) 770–780. ⇒26810.1016/j.ejor.2019.01.040
- M. K. Bahaghighat, R. Akbari et al., Fingerprint image enhancement using GWT and DMF, in 2010 2nd International Conference on Signal Processing Systems, vol. 1. IEEE, 2010, pp. V1-253–257. ⇒26810.1109/ICSPS.2010.5555771
- M. K. Bahaghighat, J. Mohammadi, Novel approach for baseline detection and Text line segmentation, International Journal of Computer Applications, 51, 2 (2012) 9–16. ⇒26710.5120/8013-1039
- M. K. Bahaghighat, F. Sahba, E. Tehrani, Textdependent Speaker Recognition by Combination of LBG VQ and DTW for Persian language. International Journal of Computer Applications, 51, 16 (2012) 23–27. ⇒26710.5120/8126-1711
- M. Bahaghighat, Q. Xin, S. A. Motamedi, M. M. Zanjireh, A. Vacavant, Estimation of wind turbine angular velocity remotely found on video mining and convolutional neural network, Applied Sciences, 10, 10 (2020) 3544. ⇒267, 28410.3390/app10103544
- M. Bahaghighat, F. Abedini, Q. Xin, M. M. Zanjireh, S. Mirjalili, Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely, Energy Reports, 2021. ⇒26810.1016/j.egyr.2021.07.077
- M. Briere, K. Oosterlinck, A. Szafarz, Virtual currency, tangible return: Portfolio diversification with bitcoin, Journal of Asset Management, 16, 6 (2015) 365–373. ⇒26710.1057/jam.2015.5
- G. M. Caporale, L. Gil-Alana, A. Plastun, Persistence in the cryptocurrency market, Research in International Business and Finance, 46 (2018) 141–148. ⇒26610.1016/j.ribaf.2018.01.002
- G. Ca yn, What is the bitcoin block size debate and why does it matter, http://www.coindesk.com/, 2015. ⇒266
- M. J. Casey, P. Vigna, Bitcoin and the digital-currency revolution, The Wall Street Journal, Jan. 23, 2015. ⇒266
- C. Chatfield, M. Yar, Holt-Winters forecasting: some practical issues, Journal of the Royal Statistical Society: Series D (The Statistician), 37, 2 (1988) 129–140. ⇒26710.2307/2348687
- A. Chaudhari, Forecasting Cryptocurrency Prices using Machine Learning, 2020, Dublin, National College of Ireland, Ph.D. dissertation. ⇒277, 282
- Z. Chen, C. Li, W. Sun, Bitcoin price prediction using machine learning: An approach to sample dimension engineering, Journal of Computational and Applied Mathematics, 365 (2020) p. 112395. ⇒26910.1016/j.cam.2019.112395
- P. Ciaian, M. Rajcaniova, D. Kancs, The economics of BitCoin price formation, Applied Economics, 48, 19 (20160) 1799–1815. ⇒26610.1080/00036846.2015.1109038
- J. Debler, Foreign initial coin o ering issuers beware: the Securities and Exchange Commission is watching, Cornell Int’l LJ, 51 (2018) 245–245. ⇒266
- J. Fiaidhi, A. Sabah, M. A. Ansari, Z. Ayaz, Bitcoin Price Prediction using ARIMA Model, 2020. ⇒270, 271, 277
- N. Gandal, H. Halaburda, Competition in the cryptocurrency market, 2014. ⇒26610.2139/ssrn.2506463
- N. Gandal, H. Halaburda, Can we predict the winner in a market with network e ects? Competition in cryptocurrency market, Games, 7, 3 (2016) 16. ⇒26610.3390/g7030016
- M. Ghorbani, M. Bahaghighat, Q. Xin, F.Özen, ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing, Journal of Cloud Computing, 9, 1 (2020) 1–12. ⇒267, 28410.1186/s13677-020-00162-1
- P. Giudici, I. Abu-Hashish, What determines bitcoin exchange prices? A network VAR approach, Finance Research Letters, 28 (2019) 309–318. ⇒26810.1016/j.frl.2018.05.013
- A. Hajikarimi, M. Bahaghighat, Optimum Outlier Detection in Internet of Things Industries Using Autoencoder, in Frontiers in Nature-Inspired Industrial Optimization. Springer, 2022, pp. 77–92. ⇒26810.1007/978-981-16-3128-3_5
- S. Hasani, M. Bahaghighat, M. Mirfatahia, The mediating e ect of the brand on the relationship between social network marketing and consumer behavior, Acta Technica Napocensis, 60, 2 (2019) 1–6. ⇒267
- G. Hileman, M. Rauchs, Global cryptocurrency benchmarking study, Cambridge Centre for Alternative Finance, 33 (2017) 33–113. ⇒26610.2139/ssrn.2965436
- I. Kaastra, M. Boyd, Designing a neural network for forecasting financial and economic time series, Neurocomputing, 10, 3 (1996) 215–236. ⇒26710.1016/0925-2312(95)00039-9
- P. Katsiampa, Volatility estimation for Bitcoin: A comparison of GARCH models, Economics Letters, 158 (2017) 3–6. ⇒26810.1016/j.econlet.2017.06.023
- M. Lischke, B. Fabian, Analyzing the bitcoin network: The first four years, Future Internet, 8, 1 (2016) 7. ⇒26610.3390/fi8010007
- M. Mudassir, S. Bennbaia, D. Unal, M. Hammoudeh, Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach, Neural Computing and Applications, pp. 1–15, 2020. ⇒26810.1007/s00521-020-05129-6733463532836901
- K. Rathan, S. V. Sai, T. S. Manikanta, Crypto-currency price prediction using decision tree and regression techniques, in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019, pp. 190–194. ⇒26710.1109/ICOEI.2019.8862585
- F. Reid, M. Harrigan, An analysis of anonymity in the bitcoin system, in Security and privacy in social networks. Springer, 2013, pp. 197–223. ⇒26610.1007/978-1-4614-4139-7_10
- D. Ron, A. Shamir, Quantitative analysis of the full bitcoin transaction graph, in International Conference on Financial Cryptography and Data Security. Springer, 2013, pp. 6–24. ⇒26610.1007/978-3-642-39884-1_2
- M. S. S. Sajadi, M. Babaie, M. Bahaghighat, Design and implementation of fuzzy supervisor controller on optimized DC machine driver, in 2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN). IEEE, 2018, pp. 26–31. ⇒26810.1109/RIOS.2018.8406627
- A. Shamseen, M. M. Zanjireh, M. Bahaghighat, Q. Xin, Developing a parallel classifier for mining in big data sets, IIUM Engineering Journal, 22, 2 (2021) 119–134. ⇒26810.31436/iiumej.v22i2.1541
- S. Siami-Namini, N. Tavakoli, A. S. Namin, A comparison of ARIMA and LSTM in forecasting time series, pp. 1394–1401, 2018. ⇒272, 27310.1109/ICMLA.2018.00227
- S. Trimborn, W. K. H¨ardle, CRIX an Index for blockchain based Currencies, Journal of Empirical Finance, 49 (2018) 107–222. ⇒26610.1016/j.jempfin.2018.08.004
- C. Trucíos, Forecasting Bitcoin risk measures: A robust approach, International Journal of Forecasting, 35, 3 (2019) 836–847. ⇒26810.1016/j.ijforecast.2019.01.003
- P. Vigna, M. J. Casey, The age of cryptocurrency: how bitcoin and the blockchain are challenging the global economic order. Macmillan, 2016. ⇒266
- H. White, Economic prediction using neural networks: The case of IBM daily stock returns, in ICNN, vol. 2, 1988, pp. 451–458. ⇒26710.1109/ICNN.1988.23959
- L. H. White, The market for cryptocurrencies, Cato J. 35 (2015) 383. ⇒266
- D. Yermack, Is Bitcoin a real currency? An economic appraisal (No. w19747), National Bureau of Economic Research, 36, 2 (2013) 843–850, ⇒266
- D. Yermack, Historical OHLC price data includes volume, www.cryptodatadownload.com ⇒269, 274