# Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price

arXiv: https://arxiv.org/abs/2003.03479 · HTML: https://mlsanity.com/paper/2003.03479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods](https://mlsanity.com/paper/2305.08105/) (2023)

## Abstract (arXiv)

In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.