ENSAE : Hidden Markov Models & Sequential Monte Carlo Methods

On particle filters applied to electricity load forecasting

Based on the article "On particle filters applied to electricity load forecasting"
Code : Antoine Grelety, Samir Tanfous, Zakarya Ali

Data treatment
- Import Temperatures.ipynb : take care of temperatures data
- utils.ipynb : add daytypes columns to RTE electricity load data
- /data : folder containing all the data
Parameters initialization
- gibbs-parameters_init_v*.ipynb : Application of Gibbs Sampling to perform parameters initialization
- elec_forecast/gibbs_sampling_model.py : Python class with Gibbs sampling methods
Models evaluation
- particle_filtering_*.ipynb : We use particle filter to estimate the model parameter and perform predictions
- elec_forecast/particle_filter.py : Python class with Particle Filter methods (PMMH, Prediction, Resampling)
- Robbins-Monro
  - parameter_estimation_robbins_monro.ipynb : notebook dedicated to parameter estimation via Robbins-Monro algorithm
  - elec_forecast/rm_estimation.py : Python class with Robbins-Monro algorithm methods

Name		Name	Last commit message	Last commit date
Latest commit History 120 Commits
data		data
elec_forecast		elec_forecast
report		report
.gitignore		.gitignore
Import Temperatures.ipynb		Import Temperatures.ipynb
README.md		README.md
gibbs-parameters_init_v4.ipynb		gibbs-parameters_init_v4.ipynb
parameter_estimation_robbins_monro.ipynb		parameter_estimation_robbins_monro.ipynb
particle_filter_3am_final.ipynb		particle_filter_3am_final.ipynb
particle_filter_3pm_final-V2.ipynb		particle_filter_3pm_final-V2.ipynb
particle_filtering_3am.ipynb		particle_filtering_3am.ipynb
particle_filtering_3pm.ipynb		particle_filtering_3pm.ipynb
utils.ipynb		utils.ipynb