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method/monotonicity.md
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Predicting Gas Usage in Blockchain Transactions</title>
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</head>
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<body>
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<h1>Predicting Gas Usage in Blockchain Transactions</h1>
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<p>
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In this context, we propose a novel method for predicting gas usage in blockchain transactions, inspired by the concept of pairwise monotonicity as detailed by Chen <a href="#chen2023address">[Chen, 2023]</a>. Unlike traditional methods like EMA, which emphasizes the forgetting of older information, our approach employs a monotonicity representation to attribute varying levels of importance to data over time. Monotonicity has demonstrated its interdisciplinary applicability, as evidenced by works such as Liu et al. <a href="#liu2020certified">[Liu et al., 2020]</a> and Milani <a href="#milani2016fast">[Milani, 2016]</a>, which focused on individual monotonicity for single variables. Our method is inspired by Chen's work <a href="#chen2023address">[Chen, 2023]</a> for introducing pairwise monotonicity in the financial domain. For instance, past due amounts over a longer period in credit scoring should more significantly impact the scoring of new debt risk. Similarly, older data points are less influential in blockchain transactions, whereas recent data points are more critical for prediction.
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</p>
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<p>
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We apply monotonicity to the <code>α</code> feature, where changes in <code>α</code> for recent blocks result in greater prediction variance than changes in distant previous data points. In the case of <code>k=2</code>, where the prediction uses data from the two previous blocks, the <code>α</code> values are <code>α<sub>1</
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