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Applied Time Series Analysis

STAT 6347

School of Natural Sciences and Mathematics

Introduction to time series data; autocorrelation function; stationarity; classical decomposition of a time series; linear processes; forecasting stationary time series; basic time series models such as autoregressive models, moving average models, ARMA models, ARIMA models and seasonal ARIMA models; model fitting; model checking; model-based forecasting; regression with ARMA errors; spectral analysis; multivariate time series; and implementation of statistical methods using a statistical software package. 3 credit hours.

Prerequisite: STAT 6337 or equivalent.

Offering Frequency: Every two years

Grades: 13

Median GPA: A

Mean GPA: 4.000

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