ARMA Models (Chapter 3)
Motivation: For `most' time series {Xt} , Wold Decomposition {Xt} is a linear TS
Xt =
j= 0
j Zt-j , {Zt} ~ WN(0,2),
= () Zt , where () = 1 + 1B + 1B2+ . . . We approxima
ARMA Modeling and Forecasting (Chap 5)
5.1 Preliminary Estimation
Useful for
order identification (requires the fitting of a number of
competing models).
initial parameter estimates for likelihood
Non-Stationary and Seasonal Time Series (Chap 6) 6.1 ARIMA Models
DEFINITION: {Xt} is an ARIMA(p,d,q) process if Yt := (1 - )d Xt is a causal ARMA(p,q) process. Remarks : 1. {Xt} satisfies the differe
Introduction to Time Series (Chapter 1) 1.1 Examples of time series
Ex 1.1.1 (Australia red wine sales; WINE.DAT)
xt = monthly sales of red wine (1000 litres) t = (Jan, 1980), (Feb, 1980), . . . , (Oc