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.
Neural spatio-temporal point processes
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AlphaEarth embeddings improve out-of-region EMS point-process forecasts 2-6x at 1-2 week histories and 10-20% at longer histories compared to event-only baselines.
citing papers explorer
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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.
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When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
AlphaEarth embeddings improve out-of-region EMS point-process forecasts 2-6x at 1-2 week histories and 10-20% at longer histories compared to event-only baselines.