library(tseries)
## Warning: package 'tseries' was built under R version 4.3.3
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
phi = numeric(25)      # Buat vektor nol sepanjang 25
phi[1]  = 0.6          # AR(1)
phi[2]  = -0.3         # AR(2)
phi[12] = 0.5          # SAR(1) dengan lag 12
phi[24] = -0.4         # SAR(2) dengan lag 24

TIME SERIES PLOT

sim_data <- arima.sim(n = 200, model = list(ar = phi), sd = 1)
ts_data <- ts(sim_data, frequency = 12)

# Plot time series
plot.ts(ts_data, main = "Simulasi SARIMA(2,0,0)(2,0,0)[12]",
        ylab = "Value", col = "blue", lwd = 2)

ACF dan PACF hingga lag-60

acf.sarima  = ARMAacf(ar = phi, ma = 0, lag.max = 60)
pacf.sarima = ARMAacf(ar = phi, ma = 0, lag.max = 60, pacf = TRUE)

acf.sarima = acf.sarima[2:61]

c1 = acf.sarima
c2 = pacf.sarima
sarima_2_0_0 = cbind(c1, c2)

print(sarima_2_0_0)
##             c1            c2
## 1   0.54451060  5.445106e-01
## 2  -0.06762628 -5.175747e-01
## 3  -0.33581715  3.669106e-03
## 4  -0.24017759  1.462449e-03
## 5  -0.01176696  2.320602e-02
## 6   0.15245634  5.511750e-02
## 7   0.17587574  3.726914e-02
## 8   0.05543268 -5.404118e-02
## 9  -0.14348524 -1.314353e-01
## 10 -0.24477513 -4.678536e-02
## 11 -0.04711516  2.203451e-01
## 12  0.38940246  3.952779e-01
## 13  0.53887739  2.669289e-03
## 14  0.27060261  3.476807e-03
## 15 -0.10981613 -2.815547e-04
## 16 -0.28933233 -9.334094e-03
## 17 -0.21688833 -1.668898e-02
## 18 -0.02808767 -7.464867e-03
## 19  0.14085856  2.838372e-02
## 20  0.21672881  7.189542e-02
## 21  0.15036396  6.250000e-02
## 22 -0.07013732 -6.666667e-02
## 23 -0.32855340 -2.857143e-01
## 24 -0.38138962 -4.000000e-01
## 25 -0.07863330 -1.815581e-16
## 26  0.22958872  4.254649e-16
## 27  0.24076201 -1.281026e-16
## 28  0.02698546 -1.827625e-16
## 29 -0.15977471 -1.504955e-16
## 30 -0.17898683  1.478390e-16
## 31 -0.05938071  1.529381e-16
## 32  0.10425896  2.565127e-17
## 33  0.21294566 -2.838534e-16
## 34  0.15933110 -2.753825e-16
## 35 -0.11371567  1.870166e-17
## 36 -0.46248453  1.227526e-16
## 37 -0.49824362 -1.072887e-16
## 38 -0.15364749  1.444440e-16
## 39  0.22159205  3.903264e-17
## 40  0.30827514  2.369263e-16
## 41  0.12535545  2.135220e-17
## 42 -0.09552762 -8.534441e-17
## 43 -0.18095698 -8.767279e-17
## 44 -0.11447795  1.031265e-19
## 45  0.03192757  1.628656e-16
## 46  0.16122041  1.332288e-16
## 47  0.16171750 -2.428198e-17
## 48 -0.03002204  8.475681e-17
## 49 -0.28419696 -1.868124e-16
## 50 -0.33017080  1.243651e-16
## 51 -0.09835217 -1.752796e-16
## 52  0.18338332 -2.070872e-17
## 53  0.26612325  4.658427e-17
## 54  0.12848988 -1.774661e-16
## 55 -0.06946926  7.708914e-17
## 56 -0.17917108  8.921569e-17
## 57 -0.15587635 -9.282172e-17
## 58 -0.02289672  9.540420e-17
## 59  0.15936989 -1.587773e-16
## 60  0.27247374 -1.137830e-16
par(mfrow = c(1, 2))
plot(acf.sarima, type = "h", xlab = "Lag", main = "ACF Plot", ylim = c(-1, 1))
abline(h = 0)

plot(pacf.sarima, type = "h", xlab = "Lag", main = "PACF Plot", ylim = c(-1, 1))
abline(h = 0)