Spring 2027

Introduction to Time Series

Introduction to Time Series covering autocorrelation, stationarity, dynamic models, ARMA models, forecasting, and seasonal adjustment with applications in finance, social sciences, and environment.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Panduan An

One student described the homework questions as insanely difficult, while another found the lecture pace too fast to copy down notes, noting the textbook was more helpful than the lectures.

Grade history

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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

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Past student reviews · Panduan An

Past students found Panduan An's lectures dull and difficult to follow, often describing them as dry, disorganized, or failing to clearly explain abstract concepts and equations.

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

Past student reviews · Panduan An

One student described the homework questions as insanely difficult, while another found the lecture pace too fast to copy down notes, noting the textbook was more helpful than the lectures.

Past student reviews · Panduan An

One student found the practice exam well-aligned with actual exams and appreciated quick email responses, while another noted that half the class dropped before the midterm due to the poor lecture quality.

Topics

  • Autocorrelation, stationarity, non-stationarity, and heteroscedasticity.
  • Dynamic models, auto-regressive and moving average models, and their identification and fitting.
  • Forecasting and seasonal adjustment techniques.
  • Applications to financial time series, social sciences, and environmental studies.

Skills

  • Identification and fitting of auto-regressive and moving average models, forecasting, and seasonal adjustment.
  • Analysis of autocorrelation, stationarity, non-stationarity, heteroscedasticity, and dynamic models.
  • Application of time series methods to financial, social science, and environmental data.

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

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

1316 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.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
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Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "3dec66aa3224bcab52a1ef72c941c4254746b521e6c5c5abbd8854d315f95815",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}