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A Simple, Transparent Model for Estimating Ethereum Transaction Speeds

This document provides a detailed examination of transaction processing times on the Ethereum blockchain. The analysis focuses on deriving a simpler, interpretable model for estimating transaction processing times.

VBy Vanessa RayMay 23, 20251 min read

This document provides a detailed examination of transaction processing times on the Ethereum blockchain. The analysis focuses on deriving a simpler, interpretable model for estimating transaction processing times.

The current estimation models, such as Etherscan Gas Tracker and EthGasStation Gas Price API, lack transparency, posing potential risks for decentralized application developers. This study aims to develop a simpler model that maintains accuracy while reducing complexity.

Choice of Prediction Algorithm: Linear regression (ordinary least squares) is used for its interpretability. Feature Engineering: A single feature is engineered, defined as the average percentage of transactions with gas prices lower than the current transaction within the last 120 blocks. Data Preprocessing: A log(x+1) transformation is applied to address data skewness. Model Validation: A sliding-time-window-based validation approach is employed. Accuracy Comparison: The new model is compared against state-of-the-practice models using absolute error metrics.

The analysis shows that the proposed model is as accurate as existing models at a global level, with specific improvements in predicting processing times for "very cheap" and "cheap" transaction categories. Additionally, potential cost savings were identified in 11.54% of sampled transactions, with a 53.9% reduction in fees.

This document provides a detailed examination of transaction processing times on the Ethereum blockchain.
Vanessa Ray · Thehackingpost

The study demonstrates that a simpler linear regression model can achieve comparable accuracy to complex existing models, with potential operational benefits in terms of cost savings and reduced reliance on opaque estimation services.

Michael Pacheco, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada Gustavo A. Oliva, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada Gopi Krishnan Rajbahadur, Centre for Software Excellence, Huawei, Canada Ahmed E. Hassan, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada

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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

Based on reporting by hackernoon.com.

AI transparency. This article was produced with the assistance of artificial intelligence and published under human editorial oversight. AI systems can make mistakes. Read how we use AI (EU AI Act, Art. 50).
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