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With the forecast package, I have a time series that I would like ?auto.arima to automatically pick the orders but I would like to coerce seasonality. The defaults for the function allow for the seasonal argument to be set to TRUE, but that only allows the option for seasonality not a coercion.
auto.arima(x, d=NA, D=NA, max.p=5, max.q=5,
max.P=2, max.Q=2, max.order=5, max.d=2, max.D=1,
start.p=2, start.q=2, start.P=1, start.Q=1,
stationary=FALSE, seasonal=TRUE,
ic=c("aicc", "aic", "bic"), stepwise=TRUE, trace=FALSE,
approximation=(length(x)>100 | frequency(x)>12), xreg=NULL,
test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"),
allowdrift=TRUE, allowmean=TRUE, lambda=NULL, biasadj=FALSE,
parallel=FALSE, num.cores=2)
968
r - Seasonality not taken account of in `auto. arima()` - Cross Validated. Stack Overflow for Teams – Start collaborating and sharing organizational knowledge.
It is better to go for SARIMA. It captures both trend and seasonality better. It captures trend with nonseasonal differencing and seasonality with seasonal differencing.
In order to deal with multiple seasonality, external regressors need to be added to the ARIMA model[1]. To incorporate the multiple seasonality in the gamer login behavior, additional Fourier terms are added to the ARIMA model, where Nt is an ARIMA process.
You can set the D parameter, which governs seasonal differencing, to a value greater than zero. (The default NA allows auto.arima() to use or not use seasonality.) For example:
> set.seed(1)
> foo <- ts(rnorm(60),frequency=12)
> auto.arima(foo)
Series: foo
ARIMA(0,0,0) with zero mean
sigma^2 estimated as 0.7307: log likelihood=-75.72
AIC=153.45 AICc=153.52 BIC=155.54
> auto.arima(foo,D=1)
Series: foo
ARIMA(0,0,0)(1,1,0)[12]
Coefficients:
sar1
-0.3902
s.e. 0.1478
sigma^2 estimated as 1.139: log likelihood=-72.23
AIC=148.46 AICc=148.73 BIC=152.21
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