Short-term prediction for Ethereum with Deep Neural Networks
The main contribution of this research is to investigate whether an Artificial Neural Network is an option to predict Ethereum cryptocurrency close price on a time constrained scenario. The ANN training time and time lagged data availability are considered as constraints on finding the fastest and the most accurate regression model using ARIMA results as a baseline. As part of the study, hourly aggregated data is processed to generate a step-ahead forecast and then processing time is compared for each architecture. Previous work related to cryptocurrency forecasting usually focus the analysis only on accuracy, and use coarser data granularity. Results have shown that convolutional neural networks over performed other architectures for accuracy and time objectives.
Alahmari, S. A. (2019). Using machine learning arima to predict the price of cryptocurrencies. The ISC International Journal of Information Security, 11(3):139–144.
Antonopoulos, A. M. and Wood, G. (2018). Mastering ethereum: building smart contracts and dapps. O’reilly Media.
Bai, S., Kolter, J. Z., and Koltun, V. (2018). An empirical evaluation of generic arXiv preprint convolutional and recurrent networks for sequence modeling. arXiv:1803.01271.
Basu, T. (2022). Ukraine is turning to online crypto crowdfunding to fund its fight against russia.
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.
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
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.
Herzen, J., Lässig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Pottelbergh, T. V., Pasieka, M., Skrodzki, A., Huguenin, N., Dumonal, M., Kóscisz, 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.
Hughes-Morgan, M., Ferrier, W. J., and Morgan, F. W. (2018). Clear signals or ambiguity? how long-buyers and short-sellers react differently to competitive actions. Journal of Managerial Issues, pages 63–81.
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.
Lim, B. and Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194):20200209.
Lo, A. W. (2007). Efficient markets hypothesis.
Lo, A. W. and MacKinlay, A. C. (2011). A non-random walk down Wall Street. Princeton University Press.
Negnevitsky, M. and Intelligence, A. (2005). A guide to intelligent systems. Artificial Intelligence.
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y. (2019). N-beats: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437.
Oreshkin, B. N., Dudek, G., Peka, P., and Turkina, E. (2021). N-beats neural network for mid-term electricity load forecasting. Applied Energy, 293:116918.
Palamalai, S., Kumar, K. K., and Maity, B. (2021). Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Review, 21(3):256–268.
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.
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.
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.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
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.