Requirements

Forecasting a Time Series in Python.ipynb

@@ -1243,9 +1243,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Auto-Arima (Py3)",
    "language": "python",
-   "name": "python3"
+   "name": "auto-arima"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1257,7 +1257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

README.md

@@ -1,2 +1,29 @@
 # Blog-Posts
-This is the Repo for the Notebooks for our Blog Posts 
+
+This is the Repo for the Notebooks for our Blog Posts.
+
+## Setup & Running
+
+To run notebooks in this repo, you'll need to recreate the kernel used to
+create them. You can do this quite simply. First, create the conda env by
+navigating to the project directory and running:
+
+```
+$ conda env create -f env.yml
+```
+
+Then you'll need to register this environment as an ipython kernel ([1]). Use the
+following command:
+
+```
+$ conda activate auto-arima
+(auto-arima) $ python -m ipykernel install --user --name auto-arima --display-name "Auto-Arima (Py3)"
+```
+
+Now you'll be able to run the notebooks via Jupyter notebook or Jupyterhub.
+
+If you don't have `conda` installed, you can get it with the `miniconda`
+distribtution ([2]).
+
+[1]: https://ipython.readthedocs.io/en/stable/install/kernel_install.html
+[2]: https://conda.io/miniconda.html

env.yml

@@ -0,0 +1,14 @@
+name: auto-arima
+channels:
+  - defaults
+dependencies:
+  - ipykernel=5.1.0
+  - matplotlib=3.0.1
+  - pandas=0.23.4
+  - python=3.6
+  - scikit-learn=0.20.0
+  - statsmodels=0.9.0
+  - pip:
+    - cufflinks==0.14.5
+    - plotly==3.3.0
+    - pyramid-arima==0.8.1