Prediction of Ethereum gas prices using DeepAR and probabilistic forecasting

References

• Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S. S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., & Wang, B. (2019). Gluonts: Probabilistic time series models in python. ArXiv abs/1906.05264.
   Google Scholar
• Blocknative (n.d.). Gas estimation for builders, by builders. https://www.blocknative.com/gas-platform
   Google Scholar
• Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In A. Moschitti, B. Pang, & W. Daelemans (Eds.), Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, a meeting of SIGDAT, a Special Interest Group of the ACL (pp. 1724–1734). ACL. https://doi.org/10.3115/v1/d14-1179
   Google Scholar
• Chuang, C., & Lee, T. (2021). A practical and economical bayesian approach to gas price prediction. In I. Awan, S. Benbernou, M. Younas, & M. Aleksy (Eds.), The international conference on deep learning, big data and blockchain (Deep-BDB 2021). Lecture Notes in Networks and Systems (Vol. 309, pp. 160–174). Springer. https://doi.org/10.1007/978-3-030-84337-3_13
   Google Scholar
• Chuang, C. (n.d.). A practical and economical of gas price prediction. https://medium.com/getamis/a-practical-and-economical-of-gas-price-prediction-d9abe955ac63
   Google Scholar
• Chuang, C. (n.d.). Official go implementation of the ethereum protocol. https://github.com/ethereum/go-ethereum/
   Google Scholar
• CRYPTO (n.d.). Binance eth to usd minute-by-minute. https://www.cryptodatadownload.com
   Google Scholar
• Ethereum blockchain (n.d.). Complete live historical ethereum blockchain data (bigquery). https://www.kaggle.com/bigquery/ethereum-blockchain
   Google Scholar
• Ethereum Gas Station (n.d.). https://github.com/ethgasstation
   Google Scholar
• Ferenczi, A. (n.d.). Using probabilistic forecasting for gas price prediction in ethereum blockchain. https://github.com/andrasfe/ethgas
   Google Scholar
• Gas Station Express Oracle (n.d.). https://github.com/ethgasstation/gasstation-express-oracle
   Google Scholar
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems (NIPS 2014) (Vol. 27). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf.
   Google Scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Mars, R., Abid, A., Cheikhrouhou, S., & Kallel, S. (2021). A machine learning approach for gas price prediction in ethereum blockchain. In 2021 IEEE 45th annual computers, software, and applications conference (COMPSAC) (pp. 156–165). IEEE. https://doi.org/10.1109/COMPSAC51774.2021.00033
   Google Scholar
• Oliveira, V. C., Valadares, J. A., de Azevedo Sousa, J. E., Vieira, A. B., Bernardino, H. S., Villela, S. M., & Gonçalves, G. D. (2021). Analyzing transaction confirmation in ethereum using machine learning techniques. ACM SIGMETRICS Performance Evaluation Review, 48, 12–15. https://doi.org/10.1145/3466826.3466832
   Google Scholar
• Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
   Web of Science ®Google Scholar
• Simionescu, S. M., Bădică, A., Bădică, C., Ganzha, M., & Paprzycki, M. (2021). On the role of blockchain in evolving the online business landscape. In 2021 conference on information communications technology and society (ICTAS) (pp. 85–90). IEEE. https://doi.org/10.1109/ICTAS50802.2021.9395045
   Google Scholar
• Singh, H. J., & Hafid, A. S. (2019). Transaction confirmation time prediction in ethereum blockchain using machine learning.
   Google Scholar
• Smith, C. (2022). Ethereum oracles. ethereum.org. https://ethereum.org/en/developers/docs/oracles/
   Google Scholar
• Wackerow, P. (2021). Erc-20 token standard. ethereum.org. https://ethereum.org/en/developers/docs/standards/tokens/erc-20/
   Google Scholar
• Ward, C. (2020). Merkle patricia tree. https://eth.wiki/fundamentals/patricia-tree
   Google Scholar
• Werner, S. M., Pritz, P. J., & Perez, D. (2020). Step on the gas? A better approach for recommending the ethereum gas price. In P. Pardalos, I. Kotsireas, Y. Guo, & W. Knottenbelt (Eds.), Mathematical research for blockchain economy, Springer proceedings in business and economics (pp. 161–177). Springer. https://doi.org/10.1007/978-3-030-53356-4_10
   Google Scholar
• Zheng, Z., Xie, S., Dai, H. N., Chen, W., Chen, X., Weng, J., & Imran, M. (2020). An overview on smart contracts: Challenges, advances and platforms. Future Generation Computer Systems, 105, 475–491. https://doi.org/10.1016/j.future.2019.12.019
   Web of Science ®Google Scholar
• Z̆unić, E., Korjenić, K., Delalić, S., & S̆ubara, Z. (2021, April). Comparison analysis of facebook's prophet, amazon's deepar+ and cnn-qr algorithms for successful real-world sales forecasting. International Journal of Computer Science and Information Technology, 13(2), 67–84. https://doi.org/10.5121/ijcsit.2021.13205
   Google Scholar