I recently started to work on "SARIMA MODELS". I am trying to reimplement an already implemented experiment which was published on a paper called "Combining neural network model with seasonal time series ARIMA Model". In the paper, they give the equation they used for SARIMA as follows: (based on ARIMA(0,1,1)(0,1,0)12, they got)
(1-B)(1-B^12)Z(t) = (1-0.88126B)a(t) Z(t) denotes the observed value at time t, a(t) is the estimated residual at time t.B is the backward shift operator.a(t) should be independently distributed as normal random variables with mean=0 and variance v^2. A part of the data set I am using is (taken by Taiwan machinery industry time series): t-14 24206.5 t-13 26075.5 t-12 21372 t-1114555 t-10 19824 t-9 21115 t-8 22414 t-7 21805 t-6 22172 t-5 22217 t-4 21255 t-3 21333 t-2 21884 t-1 25191 t 17598 Could you please tell me what is the forecast of SARIMA model at time t and the residual a(t) according to the equation and data given? Thanks! . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
