I started evaluating and comparing some methods in forecasting. I used Price of dozen eggs in US, 1900–1993, in constant dollars in the R software FMA package. I held out the last 10 years for assessment of forecast. Below are the results:
I used auto arima method in the R software. Obviously the results are way off. Am I doing something incorrect ? Below is the forecast. It does not recognize the declining trend.
I also used an unobserved components model (UCM) and obtained a good forecast, as below.
- Without outliers/level shifts there are very large standard errors and therefore wide confidence bands.
- After some iterative work, below is the output with outliers/level shifts (I know I'm overfitting here) but it did a pretty good job in forecasting; there are also narrow confidence bands.
In looking at just this example the UCM seems to predict the hold-out sample more accurately than auto.arima.
Why is auto.arima not providing a reasonable forecast?
Are state space models/UCMs better for forecasting long range?
Are there any benefits of using one method over other?