Lecture notes. Written from the material of Berkeley Stat 153, licensed CC BY 4.0. These are notes, not a transcript: the material has been reorganised and rewritten. This adaptation carries the same licence, and the original is linked above.
85. Course Overview: Time Series Analysis¶
What this covers¶
The five supplied files are the public home pages of Berkeley's Stat 153, one per semester it has run from Fall 2024 through Fall 2026. None of them contains lecture slides, a transcript, or problem-set text — each is a logistics page: instructor and GSI contacts, meeting times, and links out to a syllabus and a schedule that are not part of the supplied material. This chapter is therefore an orientation to the course itself, not to a topic in time series analysis: what the course claims to cover, how it is run, and who has run it. Nothing about trends, ARMA models, forecasting or spectra is developed further than the one-paragraph description below, because nothing further than that was in the source.
What the course covers¶
Only one of the five pages — the Spring 2026 offering, cross-listed as Stat 248 — states the course's scope directly:
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
The same page states that the course uses Python as its primary computing language. The other four home pages do not repeat this description; they list only staff, meeting times, and links to a syllabus and schedule.
The course's catalogue number pairs with a graduate-level number, Stat 248, in two of the five offerings (Fall 2026's page is titled "STAT 153 - STAT 248: Time Series Analysis"; Spring 2026's is titled "Stat 153 / Stat 248: Introduction to Time Series"). The earlier three offerings (Fall 2024, Spring 2025, Fall 2025) carry no such cross-listing in their titles.
Format: lecture and lab¶
Two of the five pages give meeting times explicitly:
- Fall 2024: lecture Tuesdays and Thursdays, 2-3:30pm (McCone 141); lab Fridays, in two two-hour sections (9-11am and 1-3pm, both in Evans Hall).
- Spring 2026: lecture Tuesdays and Thursdays, 8-9:30am (VLSB 2060); lab Fridays, in four two-hour sections spread across the morning and afternoon (Evans Hall).
Both therefore describe the same pattern: two lectures a week plus a weekly lab, run in several parallel sections to accommodate enrollment. The Spring 2025, Fall 2025 and Fall 2026 home pages do not list class or lab times on the page itself.
Staff across the five offerings¶
| Term | Instructor | GSIs / Reader |
|---|---|---|
| Fall 2024 | Ryan Tibshirani | Tiffany Ding (GSI); Theo Pan (Reader) |
| Spring 2025 | Aditya Guntuboyina | Dohyeong Ki |
| Fall 2025 | Aditya Guntuboyina | Dohyeong Ki; Shana Soohyun Kim |
| Spring 2026 | Liberty Hamilton | Nicholas Liskij; Yichen Pan |
| Fall 2026 | Aditya Guntuboyina | Aidan McLoughlin; Yichen Pan |
Aditya Guntuboyina taught three of the five offerings; Ryan Tibshirani and Liberty Hamilton each taught one. Yichen Pan is the only GSI who recurs across two listed offerings (Spring 2026 and Fall 2026).
What isn't here¶
Every home page points further than it contains. Fall 2024 links to a GitHub repository holding the
source files for lectures and homeworks, an Ed Discussion forum, and bCourses for grading; all five
pages link to a syllabus page and a schedule that in four of the five cases is rendered from an
external schedule.yml file. None of that linked content — the week-by-week topics, the readings,
the homework assignments, or the syllabus text itself — was included in the supplied material, so
none of it is reconstructed here. A chapter on the actual mathematics of trend estimation, ARMA
models, or spectral analysis as this course teaches it would need those files as input.
Sources¶
docs/statistics/berkeley/stat153/fall-2024/home.md— converted fromindex.qmd, berkeley-stat153 fall-2024, CC BY 4.0. Staff, class/lab times, and links (GitHub repo, Ed Discussion, bCourses); Schedule, Homework and Supplementary resources sections are empty in the source.docs/statistics/berkeley/stat153/fall-2025/home.md— converted fromindex.md, berkeley-stat153 fall-2025, CC BY 4.0. Staff and office hours; schedule rendered externally fromschedule.yml, not included.docs/statistics/berkeley/stat153/fall-2026/home.md— converted fromindex.md, berkeley-stat153 fall-2026, CC BY 4.0. Staff and office hours; title shows the Stat 153/Stat 248 cross-listing; schedule external, not included.docs/statistics/berkeley/stat153/spring-2025/home.md— converted fromindex.md, berkeley-stat153 spring-2025, CC BY 4.0. Staff and office hours; schedule external, not included.docs/statistics/berkeley/stat153/spring-2026/home.md— converted fromindex.md, berkeley-stat153 spring-2026, CC BY 4.0. Source of the course description, the Python computing note, class/lab times, and staff; schedule external, not included.
← 84. Evaluating Covid-19 Death Forecasts · Contents · 86. Fitting AR(p) Models to GNP →