Song L, Chen S, Meng Z, Sun M, Shang X. FMSA-SC: a fine-grained multimodal sentiment analysis dataset based on stock comment videos. IEEE Trans Multimedia. 2024;
Zhou Z, Zhou X, Qi H, Li N, Mi C. Near miss prediction in commercial aviation through a combined model of grey neural network. Expert Syst Appl. 2024;255:124690.
Liang C, Zhang Y, Li X, Ma F. Which predictor is more predictive for bitcoin volatility? And why? International Journal of Finance & Economics. 2022;27(2):1947–61.
Shumway RH, Stoffer DS, Stoffer DS. Time series analysis and its applications, vol. 3. New York: springer; 2000.
Bontempi, G., Ben Taieb, S., & Le Borgne, Y. A. (2013). Machine learning strategies for time series forecasting. Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15–21, 2012, Tutorial Lectures 2, 62–77.
Gajamannage K, Jayathilake DI, Park Y, Bollt EM. Recurrent neural networks for dynamical systems: applications to ordinary differential equations, collective motion, and hydrological modeling. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2023;33(1)
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.
Gao, T., Chai, Y., & Liu, Y. 2017 Applying long short term momory neural networks for predicting stock closing price. In 2017 8th IEEE international conference on software engineering and service science (ICSESS) (pp. 575–578). IEEE.
Roondiwala M, Patel H, Varma S. Predicting stock prices using LSTM. International Journal of Science and Research (IJSR). 2017;6(4):1754–6.
Liu, S., Liao, G., & Ding, Y. (2018). Stock transaction prediction modeling and analysis based on LSTM. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 2787–2790). IEEE.
Gu X, Chen X, Lu P, Lan X, Li X, Du Y. SiMaLSTM-SNP: novel semantic relatedness learning model preserving both Siamese networks and membrane computing. J Supercomput. 2024;80(3):3382–411.
Seabe PL, Moutsinga CRB, Pindza E. Forecasting cryptocurrency prices using LSTM, GRU, and bi-directional LSTM: a deep learning approach. Fractal and Fractional. 2023;7(2):203.
Doran MD. A forensic look at bitcoin cryptocurrency. Doctoral dissertationUtica College; 2014.
Meunier S. Blockchain 101: what is blockchain and how does this revolutionary technology work? In: Transforming climate finance and green investment with Blockchains. Academic Press; 2018. p. 23–34.
Khedr AM, Arif I, El-Bannany M, Alhashmi SM, Sreedharan M. Cryptocurrency price prediction using traditional statistical and machine-learning techniques: a survey. Intelligent Systems in Accounting, Finance and Management. 2021;28(1):3–34.
Aditya Pai B, Devareddy L, Hegde S, Ramya BS. A time series cryptocurrency price prediction using lstm. In: Emerging research in computing, information, communication and applications: ERCICA 2020, volume 2. Springer Singapore; 2022. p. 653–62.
Guo, T., Bifet, A., & Antulov-Fantulin, N. (2018). Bitcoin volatility forecasting with a glimpse into buy and sell orders. In 2018 IEEE international conference on data mining (ICDM) (pp. 989–994). IEEE.
Cavalli S, Amoretti M. CNN-based multivariate data analysis for bitcoin trend prediction. Appl Soft Comput. 2021;101:107065.
Murphy JJ. Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Penguin; 1999.
Atsalakis GS, Atsalaki IG, Pasiouras F, Zopounidis C. Bitcoin price forecasting with neuro-fuzzy techniques. Eur J Oper Res. 2019;276(2):770–80.
Article MathSciNet MATH Google Scholar
Guo H, Zhang D, Liu S, Wang L, Ding Y. Bitcoin price forecasting: a perspective of underlying blockchain transactions. Decis Support Syst. 2021;151:113650.
Yan K, Li Y. Machine learning-based analysis of volatility quantitative investment strategies for American financial stocks. Quantitative Finance and Economics. 2024;8(2):364–86.
Ortu M, Uras N, Conversano C, Bartolucci S, Destefanis G. On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert Syst Appl. 2022;198:116804.
Inder, S., & Sharma, S. (2021). Predicting the Movement of Cryptocurrency “Bitcoin” Using Random Forest. In Data Science and Computational Intelligence: Sixteenth International Conference on Information Processing, ICInPro 2021, Bengaluru, India, October 22–24, 2021, Proceedings 16 (pp. 166–180). Springer International Publishing.
Akyildirim E, Goncu A, Sensoy A. Prediction of cryptocurrency returns using machine learning. Ann Oper Res. 2021;297:3–36.
Article MathSciNet MATH Google Scholar
Goutte S, Le HV, Liu F, Von Mettenheim HJ. Deep learning and technical analysis in cryptocurrency market. Financ Res Lett. 2023;54:103809.
Yang Y, Hu X, Jiang H. Group penalized logistic regressions predict up and down trends for stock prices. The North American Journal of Economics and Finance. 2022;59:101564.
Tsai CF, Hsiao YC. Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst. 2010;50(1):258–69.
Chande TS. A time price oscillator. Technical Analysis of Stocks & Commodities. 1995;13(9):369–74.
Lambert DH. Cephalosporin hepatitis. Anesth Analg. 1980;59(10):806–7.
Wilder JW. New concepts in technical trading systems. No Title; 1978.
Williams BM. New trading dimensions: how to profit from chaos in stocks, bonds, and commodities, vol. 72. John Wiley & Sons; 1998.
Wilder JW Jr. A momentum oscillator that can help you spot market turns. Commodities. 1978;7:46–7.
Worden FG. Psychotherapeutic aspects of authority. Psychiatry. 1951;14(1):9–17.
Venkatasubramanian R, Dorsey DL. Molecular-beam epitaxial growth surface roughening kinetics of Ge (001): a theoretical study. Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena. 1993;11(2):253–8.
Kaufman SK, Chaikin M. The use of Price-volume crossover patterns in technical analysis. MTA Journal. 1991;37:35–41.
Kaufman P. Smarter trading, vol. 22. New York: McGraw-Hill; 1995.
Breiman L. Random forests Machine learning. 2001;45:5–32.
Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems science & control engineering. 2020;8(1):22–34.
Aleksandra P, Dmitry Z, Vladimir K. A study of uncertainty contribution to cryptocurrency investmen dynamics. International Journal of Technology. 2021;12(7):1529–36. https://doi.org/10.14716/ijtech.v12i7.5348.
Gandal N, Hamrick JT, Moore T, Vasek M. The rise and fall of cryptocurrency coins and tokens. Decisions Econ Finan. 2021;44(2):981–1014. https://doi.org/10.1007/s10203-021-00329-8.
Levulytė L, Šapkauskienė A. Cryptocurrency in context of fiat money functions. The Quarterly Review of Economics and Finance. 2021;82:44–54. https://doi.org/10.1016/j.qref.2021.07.003.
Ibrahim, A. (2021). Forecasting the early market movement in bitcoin using twitter's sentiment analysis: An ensemble-based prediction model. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1–5). IEEE.
Trozze A, Davies T, Kleinberg B. Explaining prosecution outcomes for cryptocurrency-based financial crimes. Journal of Money Laundering Control. 2023;26(1):172–88.
Jalan A, Matkovskyy R, Urquhart A. What effect did the introduction of bitcoin futures have on the bitcoin spot market? Eur J Financ. 2021;27(13):1251–81.
Koki C, Leonardos S, Piliouras G. Do cryptocurrency prices camouflage latent economic effects? A Bayesian hidden Markov approach. Future Internet. 2020;12(3):59.
Al-Ameer, A., & Fouad, A. S. (2021). A methodology for securities and cryptocurrency trading using exploratory data analysis and artificial intelligence. In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) (pp. 54–61). IEEE.
Cahyani, N. D. W., & Nuha, H. H. (2021). Ransomware detection on bitcoin transactions using artificial neural network methods. In 2021 9th International Conference on Information and Communication Technology (ICoICT) (pp. 1–5). IEEE.
Serrano W. The random neural network in price predictions. Neural Comput & Applic. 2022:1–19.
Luo J, Zhuo W, Xu B. A deep neural network-based assistive decision method for financial risk prediction in carbon trading market. Journal of Circuits, Systems & Computers. 2024;33(8)
Iftikhar H, Zafar A, Turpo-Chaparro JE, Canas Rodrigues P, López-Gonzales JL. Forecasting day-ahead Brent crude oil prices using hybrid combinations of time series models. Mathematics. 2023;11(16):3548. https://doi.org/10.3390/math11163548.
Carbo-Bustinza N, Iftikhar H, Belmonte M, Cabello-Torres RJ, De La Cruz ARH, López-Gonzales JL. Short-term forecasting of ozone concentration in metropolitan Lima using hybrid c
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