Eerst het belangrijkste

ARIMA-modellen in R

David Stoffer

Professor of Statistics at the University of Pittsburgh

Over mij

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

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

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

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Regressie: $Y_i = \beta X_i + \epsilon_i$, waarbij $\epsilon_i$ white noise is

White noise:

Autoregressie: $X_t = \phi X_{t-1} + \epsilon_t \ $ ($\epsilon_t$ is white noise)

Moving Average: $\epsilon_t = W_t + \theta W_{t-1} \ $ ($W_t$ is white noise)

ARMA: $X_t = \phi X_{t-1} + W_t + \theta W_{t-1} \ $

ARIMA-modellen in R