  On the Ethereum network, it is challenging to determine a gas price that
ensures a transaction will be included in a block within a user's required
timeline without overpaying. One way of addressing this problem is through the
use of gas price oracles that utilize historical block data to recommend gas
prices. However, when transaction volumes increase rapidly, these oracles often
underestimate or overestimate the price. In this paper, we demonstrate how
Gaussian process models can predict the distribution of the minimum price in an
upcoming block when transaction volumes are increasing. This is effective
because these processes account for time correlations between blocks. We
performed an empirical analysis using the Gaussian process model on historical
block data and compared the performance with GasStation-Express and Geth gas
price oracles. The results suggest that when transactions volumes fluctuate
greatly, the Gaussian process model offers a better estimation. Further, we
demonstrated that GasStation-Express and Geth can be improved upon by using a
smaller training sample size which is properly pre-processed. Based on the
results of empirical analysis, we recommended a gas price oracle made up of a
hybrid model consisting of both the Gaussian process and GasStation-Express.
This oracle provides efficiency, accuracy, and better cost.
