Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
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Updated
Jan 7, 2018 - R
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
Factoried Personalized Markov Chains for Next Basket Recommendation in R and Python
Sequential prediction learning framework and algorithm
cs2180 course artificial intelligence lab sessions
End-to-End Python implementation of Regime-Weighted Conformal (RWC) prediction for sequential VaR control in nonstationary financial markets (Schmitt, 2026). Combines kernel-based regime similarity with exponential time decay to calibrate distribution-free risk bounds. CRSP data validation, GBDT quantile forecasting, and rigorous backtesting.
A bi-directional LSTM network for sequential prediction
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