pith. sign in

arxiv: 2501.11906 · v1 · pith:R5F7VPCInew · submitted 2025-01-21 · 💻 cs.CE

Multi-source Multi-level Multi-token Ethereum Dataset and Benchmark Platform

classification 💻 cs.CE
keywords markettokenbehaviordatasetethereummethtaskforcemulti-levelmulti-source
0
0 comments X
read the original abstract

This paper introduces 3MEthTaskforce (https://3meth.github.io), a multi-source, multi-level, and multi-token Ethereum dataset addressing the limitations of single-source datasets. Integrating over 300 million transaction records, 3,880 token profiles, global market indicators, and Reddit sentiment data from 2014-2024, it enables comprehensive studies on user behavior, market sentiment, and token performance. 3MEthTaskforce defines benchmarks for user behavior prediction and token price prediction tasks, using 6 dynamic graph networks and 19 time-series models to evaluate performance. Its multimodal design supports risk analysis and market fluctuation modeling, providing a valuable resource for advancing blockchain analytics and decentralized finance research.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols

    cs.LG 2026-04 unverdicted novelty 6.0

    New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.