Projection Predictive Method for Bayesian Model Selection in Retail Time Series Forecasting

Instructions (Technical)

Preparing data and execution environment

1. Install requirements.txt and remember to activate the environment where the packages were installed before starting the jupyter kernel. It is recommended to start the kernel at the root of this project (in folder bsc-thesis) by running

   jupyter notebook $NOTEBOOK_NAME
   

2. Load the dataset from Kaggle (Corporación Favorita Grocery Sales Forecasting Competition)
3. Unzip the files into the data folder

Using MCMC sampling

Consider the notes that Facebook Prophet has documented about upstream issues with PyStan

There are upstream issues in PyStan for Windows which make MCMC sampling extremely slow. The best choice for MCMC sampling in Windows is to use R, or Python in a Linux VM.