STAT0219A-F24
Time Series Analysis
Time Series Analysis
An introduction to statistical methods for time series analysis for students with a background in statistics. Topics include time series regression, auto-regressive models, moving average models, and ARIMA models, with an emphasis on estimation and forecasting with real data applications. Students will develop skills visualizing and summarizing serially correlated data structures and fitting time series models in various statistical software packages, including R and Julia. (STAT 116 or STAT 201 and MATH 0200 concurrently, or by waiver.)
An introduction to statistical methods for time series analysis for students with a background in statistics. Topics include time series regression, auto-regressive models, moving average models, and ARIMA models, with an emphasis on estimation and forecasting with real data applications. Students will develop skills visualizing and summarizing serially correlated data structures and fitting time series models in various statistical software packages, including R and Julia. (STAT 116 or STAT 201 and MATH 0200 concurrently, or by waiver.)
- Term:
- Fall 2024
- Location:
- 75 Shannon Street 202(75SHS 202)
- Schedule:
- 12:45pm-2:00pm on Monday, Wednesday (Sep 9, 2024 to Dec 9, 2024)
- Type:
- Lecture
- Course Modality:
- In-Person
- Instructors:
- Christian Stratton
- Subject:
- Statistics
- Department:
- Mathematics & Statistics
- Division:
- Natural Sciences
- Requirements Fulfilled:
- DED
- Levels:
- Undergraduate
- Availability:
- View availability, prerequisites, and other requirements.
- Course Reference Number (CRN):
- 92754
- Subject Code:
- STAT
- Course Number:
- 0219
- Section Identifier:
- A