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1. Introduction

In this post, we will explore scalable time-series forecasting in PySpark. We will build time-series models using Convolutional Neural Network (CNN), Long Short-Term Memory, Facebook Prophet, and Seasonal ARIMA. We will then train 500 time-series Prophet models in parallel with PySpark in Google Colab. I am going to break down each step in detail, from installing packages to evaluating the models.

Let's get started!

This repository contains all the codes necessary to replicate the contents in this blog post. If you have any comments or suggestions, email me at y.s.yoon@berkeley.edu.

2. Create a Virtual Environment

Prophet only runs in Python 3.8 or a lower version. For those of us who use a higher version like myself, we can create a virtual environment and use a lower version. I've listed the detailed steps to create a virtual environment with Python 3.7.

2.1. Create and view virtual environments

  • in Windows: type "cmd" in "Type here to search" and right-click the "Command Prompt" to run it as administrator
  • in cmd: conda create -n py37prophet python=3.7
  • in cmd: conda info --envs

FYI, we can remove the virtual environment with the following command but don't run it now.

  • in cmd: conda env remove -n py37prophet

2.2. Activate the virtual environment

  • in cmd: conda activate py37prophet

FYI, we can deactivate the virtual environment with the following command but don't run it now.

  • in cmd: conda deactivate

2.3. Check the Python's Version

Make sure you have Python 3.7 installed.

  • in py37prophet: python --version

2.4. Add the virtual environment to Jupyter Notebook's list of Kernels

  • in py37prophet: pip install ipykernel
  • in py37prophet: python -m ipykernel install --user --name=py37prophet
  • in cmd or py37prophet: jupyter kernelspec list

FYI, we can remove it with the following command but don't run it now.

  • in cmd or py37prophet: jupyter kernelspec uninstall py37prophet

2.5. Start Jupyter Notebook in the virtual environment

  • in windows: In File Explorer, go to the folder you want to use and type "cmd" in the address bar
  • in cmd: conda activate py37prophet
  • in py37prophet: jupyter notebook
  • in jupyter notebook: !python --version # To check the python version

3. Install Prophet and Other Useful Packages

3.1. Install Prophet

Installing Facebook Prophet can be a headache. Make sure you have Python 3.8 or lower and follow the below instructions.

  • in windows: type "cmd" in "Type here to search" and run it as administrator
  • in cmd: conda activate py37prophet
  • in py37prophet: pip install pystan
  • in py37prophet: conda install -c conda-forge prophet

This post was very helpful for me.

3.2. Prophet asks us to install/update the following (not an absolute must)

  • pip install plotly
  • pip install jupyter
  • pip install ipywidgets

4. Implementation of Demand Forecasting in Python

Follow along in "CNN_LSTM_Prophet_SARIMA_Models.ipynb".

5. Implementation of Scalable Demand Forecasting with PySpark in Google Colab

Follow along in "Time_Series_at_Scale_with_Spark.ipynb".