R Language--time series analysis steps

Great.

(1 ) based on trend differential

Plot (lostjob,type= "B") view the overall image trend and determine how the differential

df1 = diff (lostjob) d=1 order differential

S4_df1=diff (df1,4) k=4- Step (seasonal) differential for d=1- order differential Results

(2 ) is stable according to the determined differential test

Adftest (s4_df1,lag=6) for smooth test of differential results

(3) PQ Fixed order in ARIMA (p,d,q)

ACF (S4_DF1)

PACF (S4_DF1)

(4) building an Arima model

Ans=arima (Lostjob,order=c (4,1,0), Seasonal=list (Order=c (1,0,1), period=4), Include.mean=f,fixed=c (NA,0,0,NA,NA,NA ))

(5 ) test model residuals White noise

Use natural log of T (the number Ofobservations) which provides higher power (1-beta)

Box.test (s4_df1,lag=5,type= ' Ljung ')

Box.test (ans$residuals,lag=5,type= ' Ljung ')

Or

Tsdiag (ANS)

(6 ) forecast

Predict (ans,10)

R Language-time series analysis steps