Fall 2026

Introduction to Time Series

Introduction to time series analysis covering stationarity, AR/MA models, and forecasting applications.

offering recorded3 credits
Recorded instructors · Fall 2026 Brian Powers3.4/5Haoran Xiong

Summary

1 / 6

Historical reviews of Panduan An: Reviewers describe the course as difficult, with homework questions being very hard and a high drop rate before the midterm. The lecture pace is often too fast to take notes, and prereqs do not adequately prepare students for the workload.

Grade history

average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 333, 340, graduate/professional standing, or declared in Statistics VISP

  • take one
STAT 349

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 3.17/5 from 29 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

No course-specific feedback yet.

Historical instructors & teaching patterns

Historical reviews for Panduan An describe her lectures as unclear, disorganized, and difficult to follow, often recommending the textbook instead. One reviewer contrasts this by noting her clear notes and quick email responses, though most find the teaching style frustrating.

PANDUAN AN is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction61 / 700
LEC 002Classroom Instruction1 / 20

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Student experience

the class

Historical reviews of Panduan An: Panduan An receives mixed reviews; while one student found her notes clear and email responses helpful, others describe her lectures as disorganized, dull, and ineffective. Several reviewers state that the textbook is more useful than her teaching for understanding the material.

Recent recorded grades — Fall 2023: 3.28 GPA, 59.6% A/AB (n=52 letter grades); Fall 2024: 3.31 GPA, 63.3% A/AB (n=49 letter grades); Fall 2025: 3.34 GPA, 60.4% A/AB (n=53 letter grades).

difficulty & workload

Historical reviews of Panduan An: Reviewers describe the course as difficult, with homework questions being very hard and a high drop rate before the midterm. The lecture pace is often too fast to take notes, and prereqs do not adequately prepare students for the workload.

Historical reviews of Panduan An: Students report that the textbook is essential for success, as lectures often fail to explain concepts clearly. One student found quick email responses helpful, but others note that class notes are unorganized or unavailable, forcing reliance on the book and practice exams.

Topics

  • Autocorrelation, stationarity, and heteroscedasticity
  • Dynamic, auto-regressive, and moving average models
  • Forecasting and seasonal adjustment
  • Applications in finance, social sciences, and environmental studies

Skills

  • Identifying, fitting, and forecasting time series models.
  • Analyzing stationarity, autocorrelation, and dynamic models.

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1320 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Selected offering source records
STAT 349 · Fall 2026

Introduction to Time Series

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:932:018458",
    "course_id": "STAT 349",
    "course_uid": "course_f01b96472a8cdb3029148ea7",
    "term_id": "1272",
    "source_course_id": "018458",
    "source_subject_id": "932",
    "title": "Introduction to Time Series",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "aa7198d8f1c31a28631d848a01a5a99603c1c34310339753eb128fd05f627b92",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}