Two time series, which are related to each other.
I want to see if "order" helps in predicting
sales, by running
auto.arima with and without the input of
sales<- c(15,25,37,45,53,72,77,88,97,83,92,85,113,133,124,137,131,147) order<- c(35,35,36,39,40,40,42,46,45,45,50,51,49,53,53,56,56,58) sales_ts <- ts(sales, frequency = 1, start=c(1950)) order_ts <- ts(order, frequency = 1, start=c(1950))
auto.arima without the input.
auto.arima(sales_ts) Series: sales_ts ARIMA(0,1,0) with drift Coefficients: drift 7.7647 s.e. 2.6222 sigma^2 estimated as 124.2: log likelihood=-64.59 AIC=133.18 AICc=134.04 BIC=134.85
auto.arima with the input of a series.
auto.arima(sales_ts, xreg = order_ts) Series: sales_ts Regression with ARIMA(0,0,0) errors Coefficients: intercept xreg -146.8293 5.0626 s.e. 16.4214 0.3521 sigma^2 estimated as 135.7: log likelihood=-68.67 AIC=143.35 AICc=145.06 BIC=146.02
Judged from the AIC values (133.18 vs 143.35). Am I right that the input of the
order series is not helpful in fitting the
The ultimate goal is to predict
sales. ARIMAX only provides model fitting but no forecast, so I want to see how
auto.arima can help in forecasting when combined the 2 series.