I have the weekly revenue data for an electronics company the decomposed plot of which is as follows:
I have decided to keep the seasonality and apply a suitable forecasting technique. I tried auto.arima:
> Elec <- read.xlsx("C:/Users/Himanshu.raunak/Revenue/Electronics.xlsx", 1) > Elec$Date <- as.Date(Elec$Date, format="%Y-%m-%d") > ElecTimeSeries <- ts(Elec$Revenue, frequency=52) > ElecArima <- auto.arima(ElecTimeSeries) > plot(forecast(ElecArima))
I get the following plot:
And the following warning messages:
1: In myarima(x, order = c(p, d, q), seasonal = c(P, D, Q), ... : Unable to check for unit roots
2: In myarima(x, order = c(p, d, q), seasonal = c(P, D, Q), ... : Unable to check for unit roots
3: In myarima(x, order = c(max.p > 0, d, 0), seasonal = c((m > ... : Unable to check for unit roots
and so on.
The ARIMA parameters come out to be as follows:
ARIMA(2,1,2)(0,0,1) with drift
ar1 ar2 ma1 ma2 sma1 drift 0.5282 -0.0316 -1.3125 0.3225 0.2283 2497.993 s.e. NaN NaN NaN NaN 0.0728 NaN sigma^2 estimated as 3.563e+11: log likelihood=-2931.26 AIC=5876.51 AICc=5877.1 BIC=5899.6
I realize that the AIC values are quite large.
Could you please point out at what I am doing incorrectly (warning messages and large ARIMA parameters) and provide a better solution. Also I need help understanding the ARIMA plot.