Topic 9. Time Series Analysis with Python

Here we discuss various approaches to working with time series: what types of data preparation is necessary, how to get short-term and long-term predictions. We walk through different time series models, from simple moving average to ARIMA and to general machine learning models with specific feature engineering. We also take a look at the ways to search for anomalies in time series and discuss pros and cons of these methods.

Steps in this block

1. Read 2 articles:

• “Time series analysis in Python” (same as a Kaggle Notebook);

• “Predicting future with Facebook Prophet” (same as a Kaggle Notebook);

2. Watch a video lecture on time series (optional);

3. Complete demo assignment 9 on time series analysis in Python (same as a Kaggle Notebook);

4. Check out the solution (same as a Kaggle Notebook) to the demo assignment (optional);

5. Complete Bonus Assignment 9 where you’ll engineer some features and apply a machine learning model to a time series prediction task (optional, available under Patreon “Bonus Assignments” tier).