Short-term prediction for Ethereum with Deep Neural Networks and Statistical Validation Tests

• Eduardo José Costa Lopes Centro Universitário FEI
• Reinaldo Augusto da Costa Bianchi Centro Universitário FEI

DOI: https://doi.org/10.5753/bwaif.2023.229136

Resumo

Cryptocurrency has become a popular asset in global financial markets, meaning that individual investors and asset management companies worldwide are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). Since variations on the deep learning model parameter values may also introduce variability in the results produced by the models, different statistical validations were considered part of the comparison process. Finally, this work shows that the Temporal Convolutional Network model (TCN) outperformed other architectures considered for a short-term forecast period in terms of processing time. The TCN deep learning model is also amongst the most accurate models, using an auto-regressive integrated moving average model (ARIMA) as a baseline.

Referências

Agarwal, A., Keerthana, S., Reddy, R., and Moqueem, A. (2021). Prediction of bitcoin, litecoin and ethereum trends using state-of-art algorithms. In 2021 IEEE Mysore Sub Section International Conference (MysuruCon), pages 538–545. ieee. org.

Awoke, T., Rout, M., Mohanty, L., and Satapathy, S. C. (2021). Bitcoin price prediction and analysis using deep learning models. pages 631–640.

Bai, S., Kolter, J. Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

Basu, T. (2022). Ukraine is turning to online crypto crowdfunding to fund its fight against russia.

Box, G., Jenkins, G., and Reisel, G. (2008). Time series analysis-wiley series in probability and statistics.

Chen, M., Narwal, N., and Schultz, M. (2019). Predicting price changes in ethereum. International Journal on Computer Science and Engineering (IJCSE) ISSN, pages 0975– 3397.

Chen, Y., Kang, Y., Chen, Y., and Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing, 399:491–501.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Corder, G. W. and Foreman, D. I. (2014). Nonparametric statistics: A step-by-step approach. John Wiley & Sons.

Derrick, B. and White, P. (2017). Comparing two samples from an individual likert question. International Journal of Mathematics and Statistics, 18(3).

Glassnode, W. (2022). On-chain data. https://docs.glassnode.com/general-info/on-chain-data. Accessed: 2022-06-09.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.

Hamayel, M. J. and Owda, A. Y. (2021). A novel cryptocurrency price prediction model using gru, lstm and bi-lstm machine learning algorithms. AI, 2(4):477–496.

Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer.

Herzen, J., Lässig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Pottelbergh, T. V., Pasieka, M., Skrodzki, A., Huguenin, N., Dumonal, M., Kościsz, J., Bader, D., Gusset, F., Benheddi, M., Williamson, C., Kosinski, M., Petrik, M., and Grosch, G. (2021). Darts: User-friendly modern machine learning for time series.

Lim, B., Arik, S. O., Loeff, N., and Pfister, T. (2019). Temporal fusion transformers for interpretable multi-horizon time series forecasting. arXiv preprint arXiv:1912.09363.

Lopes, E. (2022). Short-term prediction for ethereum with deep neural networks and statistical validation tests github site.

Massey Jr, F. J. (1951). The kolmogorov-smirnov test for goodness of fit. Journal of the American statistical Association, 46(253):68–78.

Oreshkin, B. N., Dudek, G., Pełka, P., and Turkina, E. (2021). N-beats neural network for mid-term electricity load forecasting. Applied Energy, 293:116918.

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., et al. (2022). Forecasting: theory and practice. International Journal of Forecasting.

Pintelas, E., Livieris, I. E., Stavroyiannis, S., Kotsilieris, T., and Pintelas, P. (2020a). Investigating the problem of cryptocurrency price prediction: a deep learning approach. In IFIP International conference on artificial intelligence applications and innovations, pages 99–110. Springer, Springer.

Pintelas, P., Kotsilieris, T., Livieris, I., Pintelas, E., and Stavroyiannis, S. (2020b). Fundamental research questions and proposals on predicting cryptocurrency prices using dnns. Technical report.

Sridhar, S. and Sanagavarapu, S. (2021). Multi-head self-attention transformer for dogecoin price prediction. In 2021 14th International Conference on Human System Interaction (HSI), pages 1–6. ieee. org.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958.

Student (1908). The probable error of a mean. Biometrika, pages 1–25.

Tanwar, S., Patel, N. P., Patel, S. N., Patel, J. R., Sharma, G., and Davidson, I. E. (2021). Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE Access, 9:138633–138646.

Würfel, M., Han, Q., and Kaiser, M. (2021). Online advertising revenue forecasting: An interpretable deep learning approach. In 2021 IEEE International Conference on Big Data (Big Data), pages 1980–1989. IEEE, ieee. org.