Improving Gas Price Predictions on Ethereum

Gas Prices on the Ethereum network can be tricky to predict. When users want their Transactions to get included in the next block, they need to know the right amount of gas to offer. If they offer too little, their transaction might be stuck for a long time. If they offer too much, they waste money. Finding the right balance is important, especially when transaction volumes start to spike.

Oracles are tools that give advice on the gas prices based on past data. However, during busy times, these oracles can get it wrong and suggest prices that are either too high or too low. This paper looks at how a specific type of statistical model, called a Gaussian process, can offer better predictions for gas prices as transaction volumes change.

#The Challenge of Gas Prices

In the Ethereum blockchain, each transaction uses gas as a form of fuel. The miners prioritize transactions that offer higher gas prices, meaning if a user wants their transaction to go through quickly, they often need to pay a higher price. But since transaction volumes can fluctuate dramatically, predicting the right gas price becomes hard.

Some existing methods for predicting gas prices rely on analyzing the transactions waiting in what is called a mempool. However, these methods require a lot of resources and can only work if the data is accurate. Other methods look at recent transactions that miners have included in blocks to suggest prices. Models like Geth and EthGasStation are based on this principle.

#The Problem with Existing Models

One of the issues with current gas price oracles is that they struggle during times of high transaction volume. When there are many transactions, these models can either suggest prices that are too low, leading to long delays, or too high, resulting in users overpaying.

For example, one model, GS-Express, suggests using the minimum gas prices from the most recent blocks. It claims to predict the likelihood that these prices will be included in the next block. However, when there is a surge of transactions, GS-Express can greatly underestimate the prices.

#The Gaussian Process Approach

This paper proposes a new model based on Gaussian processes to better predict gas prices. Gaussian processes use past data to forecast future events while also taking into account uncertainty. This makes it possible to predict not just a price, but also how likely that prediction is to be accurate.

When applied to the Ethereum gas prices, the Gaussian process can account for trends over time-like how closely related the prices of recent blocks are to each other. This allows for more stable predictions even when transaction volume is unpredictable.

#Methodology

To test this new approach, we gathered historical block data from the Ethereum network. We focused on blocks with a significant number of transactions and removed any blocks with too few transactions or unusually low fees, as these could distort the model's predictions.

Next, we fitted the Gaussian process model to this cleaned data. The idea was to use the data from a sequence of blocks to predict what the minimum gas price for the next block would be. We then compared our Gaussian process model to existing models, like GS-Express and Geth.

#Analysis and Results

Using the historical data, we compared the success rate of the Gaussian process model with that of the other two models, GS-Express and Geth. The results showed that the Gaussian process consistently provided more accurate gas price predictions when transaction volumes were high.

The Gaussian process model was able to maintain stable predictions, while the other two often failed to keep up with rapid changes in gas prices. In situations where there were many transactions, the Gaussian process could still offer reasonable predictions, which led to better success rates.

We also did a sensitivity analysis. By adjusting the amount of training data used, we found that GS-Express could perform better with smaller sample sizes when transaction volumes were steady. However, it quickly lost its edge when the volume fluctuated.

#New Gas Price Oracle

Based on our findings, we created a new gas price oracle that combines the strengths of the Gaussian process and GS-Express. This new oracle checks the current success rates from both models and adjusts the recommendations accordingly.

When gas prices are stable, GS-Express is used to keep costs down. However, when prices spike, the system switches to rely on the Gaussian process to maintain accuracy. This hybrid approach means that users can benefit from better predictions, ensuring their transactions are processed without overpaying.

#Real-World Application

While our study shows promise, future work should test this new gas price prediction model in real-time situations. This will help to check its efficiency and accuracy when users are actively making transactions.

Also, a deeper dive into different methods within Gaussian processes could be useful in finding the best approach for predicting gas prices. The goal is to make the gas price oracle as effective as possible, so users can confidently make their transactions on the Ethereum network.

#Conclusion

Gas price prediction on Ethereum is a complex task impacted by fluctuating transaction volumes. Current methods struggle during busy periods, leading to either delays or overpayment for users.

By applying Gaussian processes, we offer a better way to predict gas prices that can keep pace with changes in transaction volumes. The new hybrid gas price oracle we developed combines the strengths of existing models to create a more reliable and efficient tool for users.

As the Ethereum network continues to grow and evolve, having an effective solution for gas price prediction will be essential. Our research lays the groundwork for a more practical approach that can adapt to the dynamic nature of the blockchain. This will enhance the user experience and ensure that transactions are processed fairly and efficiently.