If you've read any of my previous posts, you'll know I don't call myself a data scientist. I would call myself a data enthusiast. Being in marketing, I make almost every business decision based on what the data is telling me.
Having said that, I ran into a great 3part tutorial series about time series in Python. It's meant for intermediate to advanced learners, but I found it was incredibly easy to follow along (even if I had to look up some of the concepts/techniques). Each video is between 1015 minutes and should only take you about 45 minutes to complete.
Here are the packages used in the tutorials:

pandas

StatsModels

matplotlib

statistics
This tutorial is taught in Python. If you are more comfortable with R, the presenter has shared the R code (and Python) script Repository
In Part 1, you will learn how to read and index your data for time series, check that the data meets the requirements or assumptions for time series modeling, and transform your data to ensure it meets those requirements. You'll primarily be using the pandas
package.
The next two parts both start right where Part 1 left off. Both don't have much of an introduction other than a really short review of what was covered in the previous section(s). If you aren't completing each tutorial right after the other, make sure to go back and review.
Part 2 has you building an Arima model using the StatsModel
package, predicting N timestamps into the future. In addition, you will also look at the Autocorrelation Function plot and Partial Autocorrelation Function plot to determine the terms in your time series model.
In the final part of the (time) series, you'll evaluate predictions using mean absolute error and Python's statistics
and matplotlib
packages. You'll plot the last five predicted and actual values, look at the differences, and calculate the mean absolute error to help evaluate your ARIMA model. At the end of the video, the presenter challanges you to improve on the model she walks you through.
I don't have the data science knowhow to make improvements, but maybe you do! I encourage you to add the ways you have improved the model to the Discussion. Who knows, maybe I'll contact you to collaborate on a follow up blog post :)
This is a companion discussion topic for the original entry at https://blog.datasciencedojo.com/timeseriespythontutorials/