I use R and the
auto.arima function for time series analysis. My data is here. Some are nonstationary, and with an apparent seasonal component. Plots of the data show trend and seasonality.
The code snippet:
library(tseries) library(forecast) t <- read.csv("tovar_moving_w.csv",header=TRUE, sep=";",dec=",") tts<-ts(t$qty,start=1, frequency=52) FR = auto.arima(tts, stepwise=FALSE, seasonal=TRUE,approximation=FALSE,trace=TRUE) fcast_l=forecast(FR,h=90,level = c(0.9,0.8,0.7)) plot(fcast_l,type="l", col="dark red", xlab="Week", main="Value") summary(FR)
auto.arima chooses the best model ARIMA(2,1,2)(1,0,0), AIC = 6541.54.
The function does not see seasonality.
If you set the D = 1
FR = auto.arima(tts, D=1, stepwise=FALSE, seasonal=TRUE,approximation=FALSE,trace=TRUE)
then the best model is ARIMA(0,0,2)(1,1,1), AIC = 5128.24.
AIC of the second model is less than that of the first. And the forecasts of the second model are essentially correct.
auto.arima not see the season? Is this an error of the OCSB test?