arXiv AI
An Open-Source, Event-Driven Pipeline for Cryptocurrency Market Data: Ingestion, Forecasting, and On-Chain Fraud Detection
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arXiv AI
Aug 21
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
arXiv:2608. 20271v1 Announce Type: new Abstract: The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls.
Hugging Face Trending Papers
Aug 20
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.
arXiv Machine Learning
Sep 29
Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]
The paper introduces a continuously synchronized dataset suite covering the entire lifecycle of decentralized prediction markets, from market creation to final settlement. It integrates market metadata, fill-level trading records, and oracle-resolution events into a unified relational system, providing over 3.29 million market records, 1.90 billion order executions, and 21 million oracle events from October 2020 to the present. The authors detail the data model, collection pipeline, and consistency mechanisms, and demonstrate the dataset’s usefulness for sports betting, economic forecasting, and blockchain research, with public access via a website and interactive tools.
arXiv Machine Learning
Jun 25
Multi-Stream Temporal Fusion for Financial Fraud Detection
arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
arXiv AI
Oct 2
Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability
arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led t...
arXiv AI
Oct 1
From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In...