A novel cryptocurrency price trend forecasting model based on LightGBM
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LightGBM has advantages of faster training speed, higher efficiency, lower memory usage, better accuracy, and has been consistently outperforming other models.
“Similarly, LightGBM191 has the advantages of faster training speed, higher efficiency, lower memory usage, better accuracy192, and has been consistently outperforming other models193,194.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [193]
Cites
1 retracted
RETRACTED: Datestamping the Bitcoin and Ethereum bubbles
Corbet, S., Lucey, B. M., & Yarovaya, L. (2017) · Finance research letters · doi:10.1016/j.frl.2017.12.006 · Cited by 667 · see citing works
RetractedLightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions
Bouri, E. I., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017) · Finance research letters · doi:10.1016/j.frl.2017.02.009 · Cited by 818 · see citing works
Can volume predict Bitcoin returns and volatility? A quantiles-based approach
Balcılar, M., Bouri, E. I., Gupta, R., & Roubaud, D. (2017) · Economic Modelling · doi:10.1016/j.econmod.2017.03.019 · Cited by 718 · see citing works
Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation
Demir, E., Gözgör, G., Lau, C. K. M., & Vigne, S. A. (2018) · Finance research letters · doi:10.1016/j.frl.2018.01.005 · Cited by 694 · see citing works
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
Soualhi, A., Medjaher, K., & Zerhouni, N. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2330494 · Cited by 670 · see citing works
Least squares twin support vector machines for pattern classification
Kumar, M., & Gopal, M. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.066 · Cited by 641 · see citing works
Portfolio diversification with virtual currency: Evidence from bitcoin
Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2018) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.03.004 · Cited by 633 · see citing works
Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold
Selmi, R., Mensi, W., Hammoudeh, S. M., et al. (2018) · Energy Economics · doi:10.1016/j.eneco.2018.07.007 · Cited by 533 · see citing works
Urquhart, A. (2017) · Economics Letters · doi:10.1016/j.econlet.2017.07.035 · Cited by 354 · see citing works
Time-varying long-term memory in Bitcoin market
Jiang, Y., Nie, H., & Ruan, W. (2017) · Finance research letters · doi:10.1016/j.frl.2017.12.009 · Cited by 296 · see citing works
Ji, Q., Bouri, E. I., Gupta, R., & Roubaud, D. (2018) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2018.05.016 · Cited by 262 · see citing works
Geng, Y., Chen, J., Fu, R., Bao, G., & Pahlavan, K. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2416186 · Cited by 134 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)
Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G. (2021) · Diagnostics · doi:10.3390/diagnostics11091714 · Cited by 317 · see citing works
Alizadeh, S., Setayesh, A., Mohamadpour, A., & Bahrak, B. (2023) · Applied Network Science · doi:10.1007/s41109-023-00565-4 · Cited by 40 · see citing works
- Authors
- Sun, X., Liu, M., & Sima, Z.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Sun, Xiaolei
- Venue
- Finance Research Letters
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 32
- Pages
- 101084
- ISSN
- 1544-6123 (ISSN-L)
- OpenAlex
- W2906922093
- Cited by
- 519
- References
- 13
- Citations by year
- 2026: 34 · 2025: 78 · 2024: 107 · 2023: 114 · 2022: 79 · 2021: 53 · 2020: 38 · 2019: 16
- FWCI
- 21.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Finance research letters (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
- Chinese Academy of Sciences (Y7X111Q01)
- National Natural Science Foundation of China (71425002, 71771206)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- cryptocurrency price prediction, price trend prediction, LightGBM, gradient boosting decision tree, economic indicators, investment risk reduction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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