Datasets:

StevenJingfeng
/

FinML

@@ -6,6 +6,88 @@ RQ2: Can this dataset be extended to apply to other algorithms, enhancing its ut
 Our analysis indicates that among the four machine learning models evaluated, DNN demonstrated the best prediction performance in both fungible token airdrop and normal periods. Specifically, DNN models showed superior predictive accuracy for gas usage during these periods. The results highlighted minimal disparity in prediction loss when comparing a DNN trained on data from normal periods to a DNN trained on data specifically from the token-airdrop period. This finding suggests that DNN models exhibit robust scalability on this dataset, eliminating the need to train a new neural network specifically for token-airdrop events. In addition to evaluating DNN models, we explored the extensibility of incorporating monotonicity constraints and sentiment analysis within the Neural Additive Model (NAM). Although these enhancements did not significantly improve the predictive accuracy of the NAM model on our test dataset, the intrinsic variability and complexity of blockchain data imply that different datasets from different time periods might yield different results. This opens a significant platform for other researchers to utilize and further explore the dataset, enabling comprehensive analyses and advancing financial machine-learning models. Our contributions include developing a comprehensive dataset that integrates both on-chain and off-chain data, compatible with various machine learning algorithms for financial prediction. This dataset forms the cornerstone of a novel research framework, enabling a deeper exploration of the financial market and its mechanisms. By offering a robust and versatile dataset, we facilitate advanced exploration and optimization efforts, driving innovation and enhancing the accuracy and reliability of financial machine-learning models in blockchain technology.
 # Hypothesis Development
 ## Machine Learning Algorithm Selection

 Our analysis indicates that among the four machine learning models evaluated, DNN demonstrated the best prediction performance in both fungible token airdrop and normal periods. Specifically, DNN models showed superior predictive accuracy for gas usage during these periods. The results highlighted minimal disparity in prediction loss when comparing a DNN trained on data from normal periods to a DNN trained on data specifically from the token-airdrop period. This finding suggests that DNN models exhibit robust scalability on this dataset, eliminating the need to train a new neural network specifically for token-airdrop events. In addition to evaluating DNN models, we explored the extensibility of incorporating monotonicity constraints and sentiment analysis within the Neural Additive Model (NAM). Although these enhancements did not significantly improve the predictive accuracy of the NAM model on our test dataset, the intrinsic variability and complexity of blockchain data imply that different datasets from different time periods might yield different results. This opens a significant platform for other researchers to utilize and further explore the dataset, enabling comprehensive analyses and advancing financial machine-learning models. Our contributions include developing a comprehensive dataset that integrates both on-chain and off-chain data, compatible with various machine learning algorithms for financial prediction. This dataset forms the cornerstone of a novel research framework, enabling a deeper exploration of the financial market and its mechanisms. By offering a robust and versatile dataset, we facilitate advanced exploration and optimization efforts, driving innovation and enhancing the accuracy and reliability of financial machine-learning models in blockchain technology.
+# Operational Measures
+## Variables
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+</head>
+<body>
+<table>
+    <caption>Variable Description</caption>
+    <tr>
+        <th>Variable Name</th>
+        <th>Description</th>
+        <th>Unit</th>
+        <th>Type</th>
+    </tr>
+    <tr>
+        <td>timestamp</td>
+        <td>Recording of the time of each block</td>
+        <td></td>
+        <td>String</td>
+    </tr>
+    <tr>
+        <td>number</td>
+        <td>The number of blocks on the chain</td>
+        <td></td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>gas_used</td>
+        <td>Actual Gas Used</td>
+        <td>Gwei</td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>gas_limit</td>
+        <td>The maximum allowed gas per block</td>
+        <td>Gwei</td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>base_fee_per_gas</td>
+        <td>The base fee set for each block</td>
+        <td>Ether</td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>gas_fraction</td>
+        <td>Fraction between Gas Used and Gas Limit</td>
+        <td></td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>gas_target</td>
+        <td>The optimal gas used for each block</td>
+        <td></td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>Y</td>
+        <td>Normalized Gas Used</td>
+        <td></td>
+        <td>Numeric</td>
+    </tr>
+    <tr>
+        <td>$Y_t$</td>
+        <td>Response variable equals to the gas_fraction</td>
+        <td></td>
+        <td>Numeric</td>
+    </tr>
+</table>
+</body>
+</html>
 # Hypothesis Development
 ## Machine Learning Algorithm Selection