  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.
