variable-order-markov-models

A framework for the comparative training and evaluation of statistical and deep learning models for multi-feature categorical sequence modeling, utilizing feature fusion and automated with MLflow and Optuna integration.

machine-learning deep-learning pytorch transformer lstm hyperparameter-optimization sequence-to-sequence gpt data-fusion lstm-attention markov-models feature-fusion mlflow optuna sequence-modeling multi-features experiment-automation experiment-tracker variable-order-markov-models multi-dimensional-sequence-modeling

• Updated
• Python

TreeMemoryPredictor (TMP): Real-time sequence prediction engine built on Dynamic Suffix Tries. Implements Context Mixing and Entropy Scaling to capture variable-order dependencies without neural networks.

python machine-learning machine-learning-algorithms tree-structure tmp markov-chains sequence-prediction online-learning machine-learning-models sequence-analysis context-tree-weighting suffix-trie prediction-tree context-tree context-mixing variable-order-markov-models markov-chains-implementation no-neural-networks

• Updated
• Jupyter Notebook