{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T10:28:07Z","timestamp":1758968887813,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811592126"},{"type":"electronic","value":"9789811592133"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-9213-3_39","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T13:03:23Z","timestamp":1605099803000},"page":"502-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cryptocurrencies Price Prediction Using Weighted Memory Multi-channels"],"prefix":"10.1007","author":[{"given":"Zhuorui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Ning","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"issue":"3","key":"39_CR1","first-page":"1","volume":"1","author":"J Abraham","year":"2018","unstructured":"Abraham, J., Higdon, D., Nelson, J., Ibarra, J.: Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci. Rev. 1(3), 1 (2018)","journal-title":"SMU Data Sci. Rev."},{"key":"39_CR2","doi-asserted-by":"publisher","first-page":"101588","DOI":"10.1016\/j.resourpol.2020.101588","volume":"65","author":"Z Alameer","year":"2020","unstructured":"Alameer, Z., Fathalla, A., Li, K., Ye, H., Jianhua, Z.: Multistep-ahead forecasting of coal prices using a hybrid deep learning model. Resour. Policy 65, 101588 (2020)","journal-title":"Resour. Policy"},{"issue":"2","key":"39_CR3","first-page":"113","volume":"48","author":"R Anderson-Sprecher","year":"1994","unstructured":"Anderson-Sprecher, R.: Model comparisons and R2. Am. Stat. 48(2), 113\u2013117 (1994)","journal-title":"Am. Stat."},{"key":"39_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.frl.2019.04.019","volume":"31","author":"N Aslanidis","year":"2019","unstructured":"Aslanidis, N., Bariviera, A.F., Mart\u00ednez-Iba\u00f1ez, O.: An analysis of cryptocurrencies conditional cross correlations. Financ. Res. Lett. 31, 130\u2013137 (2019)","journal-title":"Financ. Res. Lett."},{"issue":"11","key":"39_CR5","doi-asserted-by":"publisher","first-page":"237311","DOI":"10.22161\/ijaers.4.11.20","volume":"4","author":"NA Bakar","year":"2017","unstructured":"Bakar, N.A., Rosbi, S.: Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: a new insight of bitcoin transaction. Int. J. Adv. Eng. Res. Sci. 4(11), 237311 (2017)","journal-title":"Int. J. Adv. Eng. Res. Sci."},{"key":"39_CR6","volume-title":"Time Series Analysis: Forecasting and Control","author":"GE Box","year":"2015","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)"},{"key":"39_CR7","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"39_CR8","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"39_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.intfin.2017.11.001","volume":"52","author":"P Ciaian","year":"2018","unstructured":"Ciaian, P., Rajcaniova, M., et al.: Virtual relationships: short-and long-run evidence from bitcoin and altcoin markets. J. Int. Financ. Mark. Inst. Money 52, 173\u2013195 (2018)","journal-title":"J. Int. Financ. Mark. Inst. Money"},{"key":"39_CR10","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155\u2013161 (1997)"},{"key":"39_CR11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.frl.2015.10.008","volume":"16","author":"AH Dyhrberg","year":"2016","unstructured":"Dyhrberg, A.H.: Bitcoin, gold and the dollar-a GARCH volatility analysis. Financ. Res. Lett. 16, 85\u201392 (2016)","journal-title":"Financ. Res. Lett."},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645\u20136649. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"39_CR13","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)"},{"issue":"8","key":"39_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"39_CR16","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1109\/ACCESS.2017.2779181","volume":"6","author":"H Jang","year":"2017","unstructured":"Jang, H., Lee, J.: An empirical study on modeling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information. IEEE Access 6, 5427\u20135437 (2017)","journal-title":"IEEE Access"},{"key":"39_CR17","unstructured":"Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)"},{"key":"39_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"39_CR19","doi-asserted-by":"publisher","first-page":"e0123923","DOI":"10.1371\/journal.pone.0123923","volume":"10","author":"L Kristoufek","year":"2015","unstructured":"Kristoufek, L.: What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE 10(4), e0123923 (2015)","journal-title":"PLoS ONE"},{"key":"39_CR20","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","volume":"164","author":"W Long","year":"2019","unstructured":"Long, W., Lu, Z., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowl. Based Syst. 164, 163\u2013173 (2019)","journal-title":"Knowl. Based Syst."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 339\u2013343. IEEE (2018)","DOI":"10.1109\/PDP2018.2018.00060"},{"key":"39_CR22","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807\u2013814 (2010)"},{"key":"39_CR23","unstructured":"Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system. Technical report Manubot (2019)"},{"key":"39_CR24","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"39_CR25","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.eswa.2017.12.004","volume":"97","author":"Y Peng","year":"2018","unstructured":"Peng, Y., Albuquerque, P.H.M., de S\u00e1, J.M.C., Padula, A.J.A., Montenegro, M.R.: The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Syst. Appl. 97, 177\u2013192 (2018)","journal-title":"Expert Syst. Appl."},{"key":"39_CR26","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.ribaf.2019.06.012","volume":"50","author":"IM Sifat","year":"2019","unstructured":"Sifat, I.M., Mohamad, A., Shariff, M.S.B.M.: Lead-lag relationship between bitcoin and Ethereum: evidence from hourly and daily data. Res. Int. Bus. Financ. 50, 306\u2013321 (2019)","journal-title":"Res. Int. Bus. Financ."},{"issue":"2","key":"39_CR27","first-page":"1","volume":"2","author":"Y Sovbetov","year":"2018","unstructured":"Sovbetov, Y.: Factors influencing cryptocurrency prices: evidence from bitcoin, Ethereum, dash, Litcoin, and Monero. J. Econ. Financ. Anal. 2(2), 1\u201327 (2018)","journal-title":"J. Econ. Financ. Anal."},{"key":"39_CR28","doi-asserted-by":"publisher","first-page":"101133","DOI":"10.1016\/j.intfin.2019.101133","volume":"63","author":"T Walther","year":"2019","unstructured":"Walther, T., Klein, T., Bouri, E.: Exogenous drivers of bitcoin and cryptocurrency volatility-a mixed data sampling approach to forecasting. J. Int. Financ. Mark. Inst. Money 63, 101133 (2019)","journal-title":"J. Int. Financ. Mark. Inst. Money"},{"issue":"4","key":"39_CR29","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1504\/IJWGS.2018.095647","volume":"14","author":"Z Zheng","year":"2018","unstructured":"Zheng, Z., Xie, S., Dai, H.N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352\u2013375 (2018). https:\/\/doi.org\/10.1504\/IJWGS.2018.095647","journal-title":"Int. J. Web Grid Serv."},{"key":"39_CR30","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1109\/ACCESS.2019.2962202","volume":"8","author":"J Zhou","year":"2019","unstructured":"Zhou, J., et al.: Precious metal price prediction based on deep regularization self-attention regression. IEEE Access 8, 2178\u20132187 (2019)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Blockchain and Trustworthy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-9213-3_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T00:35:27Z","timestamp":1619310927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-15-9213-3_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811592126","9789811592133"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-9213-3_39","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BlockSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Blockchain and Trustworthy Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"blocksys2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/blocksys.info\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"100","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}