Statistics 153: Introduction to Time Series

UC Berkeley, Fall 2024

Please email the GSI with any issues; the Instructor will be looped in only as-needed.

Make sure to read the syllabus. Other handy links:

Schedule

Week 1: Aug 29 Characteristics of time series: pdf, html, source
Week 2: Sep 3-5 Measures of dependence: pdf, html, source
Week 3: Sep 10-12 Regression and prediction: pdf, html, source Hw 1 due Fri Sep 13
Week 4: Sep 17-19 Regression and prediction (continued)
Week 5: Sep 24-26 Regularization and smoothing: pdf, html, source Hw 2 due Fri Sep 27
Week 6: Oct 1-3 Regularization and smoothing (continued)
Week 7: Oct 8-10 Spectral analysis: pdf, html, source Hw 3 due Fri Oct 11
Week 8: Oct 15-17 Spectral analysis (continued)
Week 9: Oct 22-24 ARIMA models: pdf, html, source Midterm on Oct 21
Week 10: Oct 29-31 ARIMA models (continued)
Week 11: Nov 5-7 ETS models: pdf, html, source Hw 4 due Fri Nov 8
Week 12: Nov 12-14 ETS models (continued on Tues; canceled on Thurs for BSTARS)
Week 13: Nov 19-21 Advanced topics: pdf, html, source Hw 5 due Fri Nov 22
Week 14: Nov 26-28 (Nothing! Enjoy Thanksgiving)
Week 15: Dec 3-5 Advanced topics (continued) Final exam on Dec 17

Homework

Supplementary resources

We will (roughly) follow some chapters of the following two books, which you can look at as supplements to the lecture notes. The first should be available to you by searching for it online through the UC Berkeley Library, and the second is freely available at the link below.

Below are two other references on time series that may be helpful as well. The first is more advanced, and the second more elementary.


This work is licensed under a Creative Commons Attribution 4.0 International License.