This repository contains the paper Improvements to Consumption Prediction: Machine Learning Methods and Novel Features as well as reference code and associated data.
Improvements to Consumption Prediction: Machine Learning Methods and Novel Features was published in the SMU Data Science Review in 2018. Link: https://scholar.smu.edu/datasciencereview/vol1/iss4/
These Jupyter Notebooks contain a portion of the associated code for the paper.
- Economic variable stationarity: pce_econ_stat.ipynb
- Sentiment variable stationarity: pce_sent_stat.ipynb
- Vector Autoregression (VAR) models: pce_var_model.ipynb
- Random Forest models: pce_rf_model.ipynb
Python 3.*
Anaconda Python distribution (recommended)
Conda Environments
Additional Python packages (included with Anaconda distribution) -jupyter -matplotlib -numpy -pandas -seaborn -sklearn -statsmodels
Reference:
macOS: https://docs.continuum.io/anaconda/install/mac-os.html
Windows: https://docs.continuum.io/anaconda/install/windows
Reference: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
Example Code: conda env create -f pce-requirements.yml --name impr-pce-env
Example Code:
macOS: conda activate impr-pce-env
Windows: activate impr-pce-env
Reference: https://jupyter-notebook.readthedocs.io/en/stable/
Example Code: jupyter notebook