FeatureSaliency Hidden Markov Model
This repository presents a partial implementation of the feature saliency HMM algorithm as proposed by Adams et al in the paper Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models.
The implementation is a modification of the GaussianHMM class of hmmlearn.
Install dependencies via pip install -r requirements.txt
- hmmlearn==0.2.0
- scikit-learn==0.19.1
The notebook FSHMM_example.ipynb has a short example on how to use the library and shows a simple test case.
- Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models: Original paper where FSHMM is presented.
- A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing: Paper that uses FSHMM for regime identification in financial markets.
- hmmlearn: python library for HMMs.
- Simultaneous Feature Selection and Parameter Estimation for Hidden Markov Models: PhD thesis with detailed derivation of the algorithm.