Goal: How does time series analysis differ from other machine learning models?

• Read the Chapter 2. Time series graphics, Chapter 7. Time series regression models and Chapter 9. ARIMA models from Forecasting: principles and practice to get the general idea of core concepts related to time series analysis.
  • The textbook contains code in R, so you don't need to pay much attention to it - we'll be doing analogous things in Python during the lab.

1. Q&A Session:
   1. How time series data differ from other data?
   2. Explain the terms: trend, seasonal, cyclic
   3. What is the difference between stationary and non-stationary time series data?
   4. What are the AR (Autoregressive) and MA (Moving Average) models?
   5. When we develop a machine learning model, we need some input feature matrix (X) and some vector (y) we want to predict. This is a bit tricky when it comes to time series. Which features can we include for this purpose?
2. Practice session:
   1. Today's lab is placed in one Jupyter Notebook: Time series analysis
3. Advanced practice session:
   1. If you want to evaluate more advanced models, go to the optional Advanced section in the notebook.

• R.J. Hyndman & G. Athanasopoulos, G. – Forecasting: principles and practice (OTexts, 2021) – interesting book, full text online, code in R
• Brockwell, P.J., Davis, R.A. (Eds.), 2002. Introduction to Time Series and Forecasting, Springer Texts in Statistics. Springer New York, New York, NY. DOI:10.1007/b97391, full text pdf – old but gold