DataCoin: Seasonal Dataset for Quantitative Strategies with Bitcoin
Abstract
Bitcoin price forecasting poses a significant challenge due to its high volatility and the influence of multiple nonlinear and non-stationary factors. Traditional time series and econometric models often fail to capture such complexities. To address this issue, this study proposed the construction of an enriched and multifactorial dataset. The analysis of the dataset revealed consistent seasonal patterns, indicating the presence of recurring effects in Bitcoin's behavior. These findings provide relevant insights for the development of more robust quantitative strategies and predictive models. The dataset and source code will be made publicly available, encouraging reuse by researchers, investors, and developers.
Keywords:
Bitcoin, Dataset, Quantitative analysis
References
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Yenidoğan, I., Çayir, A., Kozan, O., Dağ, T., and Arslan, (2018). Bitcoin forecasting using arima and prophet. In 2018 3rd International Conference on Computer Science and Engineering (UBMK).
Zhang, H., Nettleton, D., and Zhu, Z. (2019). Regression-enhanced random forests.
Bagul, J., Warkhade, P., Gangwal, T., and Mangaonkar, N. (2022). Arima vs lstm algorithm – a comparative study based on stock market prediction. In 2022 5th International Conference on Advances in Science and Technology (ICAST).
Bâra, A., Georgescu, I. A., Oprea, S.-V., and Cristescu, M. P. (2024). Exploring the dynamics of brent crude oil, s&p500 and bitcoin prices amid economic instability. IEEE Access.
De Leon, L. G. N., Gomez, R. C., Tacal, M. L. G., Taylar, J. V., Nojor, V. V., and Villanueva, A. R. (2022). Bitcoin price forecasting using time-series architectures. In 2022 International Conference on ICT for Smart Society (ICISS).
Greaves, A. and Au, B. (2015). Using the bitcoin transaction graph to predict the price of bitcoin. No data, 8:416–443.
Guo, H., Gao, K., Yu, Y., Liu, Y., and Fu, L. (2022). A diluted bitcoin-dollar-gold mean prediction scheme based on periodic prediction method. IEEE Access.
Hajare, R., Puri, C., and Gote, P. M. (2025). Bitcoin price prediction using deep learning models. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL).
Hajek, P. and Olej, V. (2023). Hierarchical intuitionistic tsk fuzzy system for bitcoin price forecasting. In 2023 IEEE International Conference on Fuzzy Systems (FUZZ).
Iqbal, M., Iqbal, A., Alshammari, A., Ali, I., Maghrabi, L. A., and Usman, N. (2024). Sell or hodl cryptos: Cryptocurrency short-to-long term projection using simultaneous classification-regression deep learning framework. IEEE Access.
Kristoufek, L. (2015). What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis. PLOS ONE.
Mahfooz, A. and Phillips, J. L. (2024). Conditional forecasting of bitcoin prices using exogenous variables. IEEE Access.
Mallqui, D. C. and Fernandes, R. A. (2019). Predicting the direction, maximum, minimum and closing prices of daily bitcoin exchange rate using machine learning techniques. Applied Soft Computing.
Muminov, A., Sattarov, O., and Na, D. (2024). Enhanced bitcoin price direction forecasting with dqn. IEEE Access.
Nayak, S. C., Das, S., Dehuri, S., and Cho, S.-B. (2023). An elitist artificial electric field algorithm based random vector functional link network for cryptocurrency prices forecasting. IEEE Access.
Rafi, M., Mirza, Q. A. K., Sohail, M. I., Aliasghar, M., Aziz, A., and Hameed, S. (2023). Enhancing cryptocurrency price forecasting accuracy: A feature selection and weighting approach with bi-directional lstm and trend-preserving model bias correction. IEEE Access.
Raman, R., Kumar, V., Pillai, B. G., Rabadiya, D., Divekar, R., and Vachharajani, H. (2024). Forecasting bitcoin value with hybrid lstm-gru neural networks. In 2024 Second International Conference on Data Science and Information System (ICDSIS).
Ren, X., Jiang, W., Duan, K., and Mishra, T. (2025). Being an emotionally unaffected investor: Evidence from bitcoin. IEEE Transactions on Engineering Management.
S, S. A., D, R., and Devi, V. S. K. (2025). Forecasting bitcoin price trends: Integrated machine learning with market trends. In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT).
Sattarov, O., Jeon, H. S., Oh, R., and Lee, J. D. (2020). Forecasting bitcoin price fluctuation by twitter sentiment analysis. In 2020 International Conference on Information Science and Communications Technologies (ICISCT).
Shang, L. (2025). Sentiment-driven bitcoin price range forecasting: Enhancing cart decision trees with high-dimensional indicators and twitter dynamics. IEEE Access.
Sujatha, R., Mareeswari, V., Chatterjee, J. M., Mousa, A. A. A., and Hassanien, A. E. (2021). A bayesian regularized neural network for analyzing bitcoin trends. IEEE Access.
Sutiksno, D. U., Ahmar, A. S., Kurniasih, N., Susanto, E., and Leiwakabessy, A. (2018). Forecasting historical data of bitcoin using arima and asutte indicator. Journal of Physics: Conference Series.
Vysotska, V., Smelyakov, K., Naumov, A., and Shtanko, V. (2024). A study of the effectiveness of using information technology based on artificial intelligence for bitcoin price forecasting. In 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT).
Waheeb, W., Shah, H., Jabreel, M., and Puig, D. (2020). Bitcoin price forecasting: A comparative study between statistical and machine learning methods. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS).
Wang, M., Braslavski, P., Manevich, V., and Ignatov, D. I. (2025). Bitcoin ordinals: Bitcoin price and transaction fee rate predictions. IEEE Access.
Waseem, M. and Singh, S. N. (2024). Advanced predictive modelling for bitcoin and ethereum price dynamics. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I).
Wu, C.-H., Lu, C.-C., Ma, Y.-F., and Lu, R.-S. (2018). A new forecasting framework for bitcoin price with lstm. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW).
Yang, F., Qiao, Y., Bo, J., Ye, L., and Abedin, M. Z. (2024). Blockchain and digital asset transactions-based carbon emissions trading scheme for industrial internet of things. IEEE Transactions on Industrial Informatics.
Yenidoğan, I., Çayir, A., Kozan, O., Dağ, T., and Arslan, (2018). Bitcoin forecasting using arima and prophet. In 2018 3rd International Conference on Computer Science and Engineering (UBMK).
Zhang, H., Nettleton, D., and Zhu, Z. (2019). Regression-enhanced random forests.
Published
2025-09-29
How to Cite
CORDEIRO, José Jeovane R.; ARAÚJO, Arlino H. Magalhães de; AVELINO, Guilherme A..
DataCoin: Seasonal Dataset for Quantitative Strategies with Bitcoin. In: DATASET SHOWCASE WORKSHOP (DSW), 7. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 34-45.
DOI: https://doi.org/10.5753/dsw.2025.247689.
