Fall 2026

Applied Time Series Analysis, Forecasting and Control I

STAT 701 covers the theory and application of discrete time series models, focusing on forecasting, model building, and diagnostic checking.

offering recorded3 credits
Recorded instructors · Fall 2026 Christopher Geoga

Summary

1 / 3

Recent recorded grades — Spring 2022: 3.88 GPA, 94.1% A/AB (n=17 letter grades); Fall 2023: 4.00 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 3.67 GPA, 77.8% A/AB (n=18 letter grades).

Grade history

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average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
STAT 701 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

Recent recorded grades — Fall 2023: 4.00 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 3.67 GPA, 77.8% A/AB (n=18 letter grades).

Historical instructors & teaching patterns

CHRISTOPHER GEOGA is recorded teaching in Fall 2023, Spring 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

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction29 / 301

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

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2022: 3.88 GPA, 94.1% A/AB (n=17 letter grades); Fall 2023: 4.00 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 3.67 GPA, 77.8% A/AB (n=18 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Dynamic relations by difference equations
  • Autoregressive integrated Moving Average (ARIMA) models
  • Seasonal model application

Skills

  • Theory and application of discrete time series models
  • Principles of iterative model building
  • Identification, fitting, and diagnostic checking of models
  • Seasonal model application to forecasting

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

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
STAT 701 · Fall 2026

Applied Time Series Analysis, Forecasting and Control I

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": "1272:932:018489",
    "course_id": "STAT 701",
    "course_uid": "course_98c8f7223e5e4f977f8032c8",
    "term_id": "1272",
    "source_course_id": "018489",
    "source_subject_id": "932",
    "title": "Applied Time Series Analysis, Forecasting and Control I",
    "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.

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "778ce63e5edfdf12d6a817554ad1830297bc9578fc0e8b178638efa8dfcaf7b2",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}