Understanding Ethereum gas prices is crucial for users navigating the blockchain’s transaction ecosystem. As the cost of executing operations on the Ethereum network, gas prices directly influence confirmation speed and user experience. This article explores the statistical behavior of Ethereum gas prices, focusing on key patterns, modeling techniques, and predictive insights derived from real-world data between January 2018 and October 2019.
The study centers around a refined metric called the threshold gas price, designed to reflect the minimum cost required for a transaction to be included in a block under varying miner strategies. Through descriptive analysis and advanced time-series modeling, we uncover critical trends in user behavior, network demand elasticity, and daily usage cycles.
The Role of Gas in Ethereum Transactions
In the Ethereum network, every computational action—whether transferring tokens, deploying smart contracts, or interacting with decentralized applications—requires a certain amount of gas. Gas is a unit that measures the computational effort required to execute operations. Users set a gas price, typically denominated in Gwei (1 Gwei = 10⁻⁹ ETH), which determines how much they are willing to pay per unit of gas.
Once broadcasted, transactions enter a pool of pending transactions. Miners then select which transactions to include in the next block based on profitability. Since miners earn transaction fees (gas price × gas used), higher-priced transactions are generally prioritized.
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However, not all miners follow purely profit-driven selection rules. Some prioritize transactions from specific addresses or apply custom logic, leading to inconsistencies in how gas prices translate into confirmation likelihood. This variability necessitates a more robust measure than the raw minimum gas price.
Defining the Threshold Gas Price
To account for diverse miner behaviors, this analysis introduces the threshold gas price—a more reliable indicator of the effective market-clearing price for transaction inclusion.
The threshold gas price ($P_{gas}$) is defined as:
- $P_{min}$ if the minimum gas price in a block exceeds 1 Gwei
- $P_{q1}$ (first quartile gas price) otherwise
This adjustment acknowledges that extremely low minimum prices may result from non-market-driven miner behavior (e.g., priority given to affiliated addresses). By switching to the first quartile when necessary, the metric better reflects the true cost of securing block inclusion.
Core keywords naturally integrated: Ethereum, gas price, confirmation time, transaction fees, blockchain, network congestion, miner behavior, forecasting model
Key Empirical Observations
Short-Term Inelastic Demand
Analysis of over 3.8 million blocks reveals that demand for Ethereum transaction space is highly inelastic in the short term. The average threshold gas price during the study period was 6.6 Gwei, with a standard deviation of 19.9 Gwei—indicating extreme volatility. More telling is the skewness: the median stood at just 3.0 Gwei, while spikes pushed prices far above average.
This pattern suggests users are often impatient and willing to overpay during peak congestion. With block capacity fixed by the gas limit, sudden surges in demand lead to rapid price escalations, characteristic of an inelastic market.
Behavioral Pricing Patterns
A striking behavioral trend emerges in how users set gas prices. The distribution shows pronounced peaks at 10 Gwei and 20 Gwei, round numbers that appear psychologically appealing. Over 75% of minimum gas prices and 70% of median values are whole integers in Gwei.
While setting a gas price slightly above an integer (e.g., 10.2 Gwei) could improve inclusion chances without significantly increasing fees, most users stick to round figures. This reflects limited strategic thinking about fee optimization—similar to patterns seen in economic "guessing games" where participants converge on salient numbers.
Day-Night Activity Cycle
Network usage follows a clear daily rhythm tied to European business hours (7 a.m. to 6 p.m. UTC). During this window, both transaction volume and gas prices rise noticeably. The utilization rate—the proportion of block capacity used—peaks in the afternoon UTC, confirming heightened activity.
This cyclical pattern implies that predictable demand fluctuations can inform smarter transaction scheduling and pricing decisions.
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Statistical Modeling: SARIMA for Gas Price Forecasting
Given the volatility of individual block-level gas prices, the study uses the hourly median threshold gas price as the primary time series for modeling. This smoothed metric correlates strongly with the probability of confirmation within a target timeframe.
Model Selection: Seasonal ARIMA (SARIMA)
After applying a Box-Cox transformation to stabilize variance, the data revealed daily seasonality and stationarity following 24-hour differencing. Based on partial autocorrelation analysis and AIC comparisons, the optimal model identified was:
ARIMA(2,0,1)(0,1,1)[24]
This model includes:
- Two non-seasonal autoregressive terms
- One non-seasonal moving average term
- One seasonal moving average term (24-hour cycle)
It effectively captures both short-term dependencies and recurring daily patterns in gas price movements.
Model Performance
Backtesting across 500 random training-test splits showed that the SARIMA model outperformed naïve and seasonal naïve benchmarks in predicting hourly median gas prices over a 24-hour horizon. The mean absolute percentage error (MAPE) was significantly lower, demonstrating superior accuracy.
Additionally, models fitted to the first and third quartiles of hourly gas prices yielded similar MAPE values, suggesting the framework generalizes well across different confidence levels for confirmation time.
Practical Implications for Users
For Ethereum users, these findings offer actionable insights:
- Timing matters: Submitting transactions during off-peak hours (evening UTC) can reduce costs.
- Pricing strategically: Using non-integer Gwei values may improve inclusion odds without overpaying.
- Predictive tools work: Statistical models like SARIMA can guide dynamic fee estimation services.
Although Ethereum has since transitioned to proof-of-stake (Ethereum 2.0), reducing base fee volatility through EIP-1559, understanding historical gas dynamics remains valuable for analyzing legacy data, optimizing Layer 2 solutions, and building adaptive dApps.
Frequently Asked Questions (FAQ)
Q: What is the threshold gas price?
A: It’s a refined metric that estimates the minimum gas price needed for transaction inclusion, adjusting for non-standard miner behaviors by using either the block’s minimum or first-quartile gas price.
Q: Why is demand for Ethereum gas considered inelastic?
A: Because users often pay high fees during congestion without delaying transactions, indicating low sensitivity to price changes in the short term.
Q: How accurate is the SARIMA forecasting model?
A: Backtesting shows it outperforms simple benchmarks, with lower prediction errors over 24-hour horizons—making it suitable for real-time fee recommendation systems.
Q: Does this research still apply after Ethereum’s shift to proof-of-stake?
A: While EIP-1559 changed fee mechanics, understanding pre-upgrade patterns helps contextualize current behavior and informs modeling for alternative chains and Layer 2 networks.
Q: Can behavioral pricing affect my transaction speed?
A: Yes—choosing less common gas prices (like 10.3 Gwei instead of 10.0) may reduce competition with other similarly priced transactions, improving chances of faster inclusion.
Q: How does daily usage pattern impact gas prices?
A: Network activity peaks during European business hours (UTC), leading to higher congestion and elevated gas prices; scheduling transactions outside these times can save costs.
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