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.
Cryp- tomixer: Fine-grained market information-aware mlp networks for individual cryptocurrency trading prediction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
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.