DATASCI 531 - Modeling and Analysis of Time Series Data
Winter 2022, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Data Science (DATASCI)
Department: LSA Statistics
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Requirements & Distribution:
Waitlist Capacity:
Advisory Prerequisites:
STATS 500 or equivalent and co-requisite 511.
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May not be repeated for credit.
Primary Instructor:


Introduction to time series analysis using time-domain and frequency-domain methods including: auto-regressive moving average (ARMA) models, regression with ARMA errors, estimation of the spectral density, state-space model techniques for fitting structured dynamic models to time series data. Examples will be drawn from ecology, economics, epidemiology, finance and elsewhere.

Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc.; stochastic difference equations, autoregressive schemes, moving averages; large sample inference and prediction; covariance structure and spectral densities; hypothesis testing and estimation and applications and other topics.



DATASCI 531 - Modeling and Analysis of Time Series Data
Schedule Listing
001 (LEC)
 In Person
60Enrollment Management
TuTh 10:00AM - 11:30AM
Note: The Department of Statistics must allow students who are in its programs the opportunity to register for Statistics classes prior to removing reserved seats. Any remaining seats will be taken off of reserve on December 18, 2019. Instructors in the Statistics Department do not manage their waitlists and do not issue overrides.

Textbooks/Other Materials

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Syllabi are available to current LSA students. IMPORTANT: These syllabi are provided to give students a general idea about the courses, as offered by LSA departments and programs in prior academic terms. The syllabi do not necessarily reflect the assignments, sequence of course materials, and/or course expectations that the faculty and departments/programs have for these same courses in the current and/or future terms.

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