A Machine Learning Approach for Gas Price Prediction in Ethereum Blockchain

Abstract

Ethereum is a blockchain-based platform that pro-vides a global computational infrastructure to run smart contracts. In order to assign a cost to smart contract and transaction execution, the Ethereum Blockchain adopts a gas-based metering approach which is designed to motivate miners to operate the network and protect it against attacks. More precisely, miners receive fees from all transactions included in the mined block in addition to the mining reward. Hence, the higher the gas price in the transactions, the higher the fee paid to the miner will be, resulting in faster selection and execution of higher priced gas transactions. Therefore, an Ethereum transaction sender is exposed to the non-trivial task of having to choose an optimal gas price, as underpaying likely results in a transaction not being picked by miners, whereas overpaying leads to superfluous costs. This paper provides recommendation approach that proposes an appropriate gas price to users. More precisely, it investigates different approaches of forecasting algorithms applied for gas price predictions for the next block in Ethereum Blockchain. The gas price is predicted using the Prophet model and the deep learning models, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). It also aims to compare these approaches with the most used gas price oracles. An evaluation of the obtained results show that the LSTM and GRU proposed models outperform Prophet model as well as the gas price oracle Geth. In this case, LSTM and GRU provide a low mean squared error (MSE) of 0,008 whereas Geth gives an MSE of 0.016 and Prophet gives an MSE of 0.014.