Evaluating Ethereum Gas Fee Dynamics

Abstract

Ethereum transaction fees exhibit substantial shortterm volatility driven by network congestion, making it difficult for users and applications to determine optimal transaction timing. This work investigates the temporal structure of Ethereum base fees and develops a scalable data-collection and forecasting pipeline for short-horizon, congestion-aware fee estimation. We propose a harvester engine framework based on a parallel blockprocessing mechanism to capture short-term market volatility and develop a parallelized harvester for efficient fee-history collection using the eth_feeHistory JSON-RPC interface. This interface provides the high-resolution, block-level data required for intraday analysis, despite protocol constraints such as the$\mathbf{1 0 2 4}$-block retrieval limit per request. Our fee-history engine incorporates bounded concurrency, latency-aware pacing, and retry stabilization, reducing 30-day data-acquisition time from hours to minutes. We analyze intra-day fee behavior and show that Ethereum base fees exhibit a stable$\mathbf{2 4}$-hour diurnal cycle. We also propose a normalized shape with a rolling-level calibration framework that preserves a stable daily rhythm while continuously adapting to month-level fee changes. Empirical evaluation shows that the adaptive approach improves accuracy and robustness. These findings demonstrate that Ethereum gas fees contain a predictable structure that can be leveraged for practical, short-term forecasting when combined with adaptive calibration.