Statistics
 

Applied Time Series and Forecasting

Applied Time Series and Forecasting
Data mining, univariate ARIMA time series theory and application, seasonal models, spatial correlation models, conditional heteroscedastic models in financial time series, case studies.
STAT
469
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 330 & STAT 340
 Taught 
 ProgramsContaining STAT 469
Course Outcomes: 

Define Functions

Define the autocovariance and autocorrelation functions

Define Weak Stationarity

Define weak stationarity

Define Process

Define white noise process

AR and MA Model

Derive the mean, autocovariance, and autocorrelation of an AR and MA model

ARMA

Define an ARMA (p, q) process

Order of an ARIMA

Select the order of an ARIMA model from the sample ACF and sample PACF

Fit an ARIMA

Fit an ARIMA (p, d, q) model and generate forecasts with prediction intervals in R