Model ARIMA di R
David Stoffer
Professor of Statistics at the University of Pittsburgh

astsa
library(astsa)
plot(jj, main = "Johnson & Johnson Quarterly Earnings per Share", type = "c")
text(jj, labels = 1:4, col = 1:4)

library(astsa)
plot(globtemp, main = "Global Temperature Deviations", type= "o")

library(xts)
plot(sp500w, main = "S&P 500 Weekly Returns")

Regresi: $Y_i = \beta X_i + \epsilon_i$, dengan $\epsilon_i$ adalah white noise
White Noise:
AutoRegresi: $X_t = \phi X_{t-1} + \epsilon_t \ $ ($\epsilon_t$ adalah white noise)
Moving Average: $\epsilon_t = W_t + \theta W_{t-1} \ $ ($W_t$ adalah white noise)
ARMA: $X_t = \phi X_{t-1} + W_t + \theta W_{t-1} \ $
Model ARIMA di R