simulacion de ruido blanco

g=rnorm(100,0,1)
g
##   [1] -2.06734761 -1.61421945 -0.09297648  2.55877394  0.91899028
##   [6]  2.79611982 -0.30680528 -0.73710006 -0.85098799 -0.73043284
##  [11] -0.91692899  0.95589790  0.30619868  0.24217046 -1.03826931
##  [16] -1.63629192 -0.47266717  2.05937776  0.11076363 -0.40323386
##  [21]  1.24221998 -0.07300865  1.65293848 -0.79071596 -0.12130190
##  [26] -0.15358563 -0.44218211  0.75897113 -0.15685554 -1.90465687
##  [31]  0.40203483 -0.77963515 -1.66504643  0.88393897  0.49980154
##  [36] -0.51533100  0.59238967 -1.20985264 -1.78045702  0.51395025
##  [41]  0.04388270  0.09534893  0.32711611  0.30943209 -0.97514884
##  [46]  0.82637780 -0.22925968  0.20881258 -1.12748994 -1.56113371
##  [51]  1.03199439 -1.82769270  0.50417465  0.58884567  0.30206624
##  [56] -1.65329137  0.92478286  0.32603695 -0.12198221  1.62864138
##  [61] -0.01384649 -0.76547226 -0.59017354  0.65520705  0.71320414
##  [66]  2.45776984  0.14235336 -1.03902196  0.10248758 -0.24456097
##  [71] -1.13365450 -0.97453036  0.16486515  1.24923944  0.01058647
##  [76]  0.60940327  0.64429970  0.66104608  0.90571775 -0.57229677
##  [81]  0.39362015  0.23136218 -1.41753569  0.85508186  2.38295268
##  [86]  0.49034757  0.86354855 -0.18038512 -1.66926782 -0.14693246
##  [91]  1.44225701 -0.32393846 -0.40785725  0.39525292  0.27788995
##  [96] -1.33092188 -1.28876775  1.43763385 -1.17091345 -0.87253344

Grafica de secuencia del ruido blanco

ts.plot(g,main=" Grafica de secuancia de proceso ruido blanco")

Graficando el ACF Y PACF

## usando una matriz de 1x2
par(mfrow=c(1,2))
acf(g,main=" Grafica de autocorrelacion simple",ylim=c(-1,1))
pacf(g,main=" Grafica de autocorralcion parcial",ylim=c(-1,1))

## Generando un camino aleatorio

w=rnorm(100,0,1)
x<-w
for(t in 2:100) x[t]<-x[t-1]+w[t]
## haciendo la grafica del camino aleatorio
ts.plot(x,main=" Generacion de camino aleatorio Xt")

acf(x,main=" Grafica de autocorrelacion simple",ylim=c(-1,1))

pacf(x,main=" Grafica de autocorralcion parcial",ylim=c(-1,1))

Generando un AR(1) phi=0.8

layout(matrix(c(1,1,2,3) ,2,2,byrow=TRUE))
AR<-arima.sim(list(order=c(1,0,0),ar=+.8),n=100)
plot(AR,main=(expression(AR(1)~~~~phi==0.8)))
acf(AR,main="autocorelacion simple de orden 1")
pacf(AR,main="autocorrealacion parcial")

Generando un AR(1) phi=-0.8

layout(matrix(c(1,1,2,3) ,2,2,byrow=TRUE))
AR1<-arima.sim(list(order=c(1,0,0),ar=-.8),n=100)
plot(AR1,main=(expression(AR(1)~~~~phi==-0.8)))
acf(AR1,main="autocorelacion simple de orden 1")
pacf(AR1,main="autocorrealacion parcial")

Generando un Ma(1) theta=0.3

layout(matrix(c(1,1,2,3) ,2,2,byrow=TRUE))
MA<-arima.sim(list(order=c(0,0,1),ma=.3),n=100)
plot(MA,main=(expression(MA(1)~~~~theta==-0.8)))
acf(MA,main="autocorelacion simple de orden 1")
pacf(MA,main="autocorrealacion parcial")

``` ### Generando un MA(2)

layout(matrix(c(1,1,2,3) ,2,2,byrow=TRUE))
MA2<-arima.sim(list(order=c(0,0,2),ma=c(.3,.2)),n=100)
plot(MA2,main=(expression(MA(2)~~~~theta==c(.3,.2))))
acf(MA2,main="autocorelacion simple de orden 2 ")
pacf(MA2,main="autocorrealacion parcial")