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CryptoL is a unified framework for cryptocurrency multivariate time series forecasting that mitigates scale dominance and enforces physical constraints to enhance prediction accuracy and financial validity.
This paper presents a benchmark for predicting institutional equity holdings using temporal graph machine learning, framing it as node affinity prediction on a bipartite graph. The proposed NAVIS model achieves state-of-the-art NDCG of 0.9127 on a dataset from SEC Form 13F filings.