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.

翻译：在以太坊网络中，确定一个既能确保交易在用户要求的时间范围内被纳入区块，又不会支付过高的天然气价格是一项挑战。解决此问题的一种方法是使用基于历史区块数据推荐天然气价格的天然气价格预言机。然而，当交易量快速增加时，这些预言机往往低估或高估价格。在本文中，我们展示了高斯过程模型如何在交易量上升时预测下一区块中最低价格的分布。这种方法之所以有效，是因为这些过程考虑了区块之间的时间相关性。我们使用高斯过程模型对历史区块数据进行了实证分析，并与GasStation-Express和Geth天然气价格预言机进行了性能比较。结果表明，当交易量波动较大时，高斯过程模型能提供更准确的估计。此外，我们证明了通过使用适当预处理后更小的训练样本量，可以改进GasStation-Express和Geth。基于实证分析结果，我们推荐了一种由高斯过程与GasStation-Express混合模型构成的天然气价格预言机。该预言机兼具效率、准确性和更好的成本效益。