Note that I do most of my analysis using R and Excel.
Let's take this data set for example. I modified it as the data itself is proprietary: the years are also different:
1967 2,033,407 1968 2,162,275 1969 2,159,640 1970 2,312,352 1971 2,554,449 1972 2,548,425 1973 2,101,225 1974 1,951,944 1975 2,106,250 1976 1,687,625 1977 1,636,496 1978 1,494,525 1979 1,606,825 1980 1,460,937 1981 1,310,494 1982 1,319,750 1983 1,263,643 1984 1,171,656 1985 1,194,950
What I usually do:
- A linear regression
- Some form of polynomial trending
- Moving average and double moving average
- Basic ARIMA using p = 1, q = 0.
- I calculate the errors for all these as well
- I average all the forecasts out and the error to have my final result.
Note that I'm an engineer that wants to get into statistics and the ability to properly validate and calibrate my models.
What is the correct way to forecast this to 5, 10, or even 15 future years?
In a way I'm looking to move beyond the plugging data into a model and believe the data. Yes, I'm aware I can look at the errors. I mainly use RMSE or MAE. But I still am not confident when it comes to just predicting data the right way.
this is also related to this question I posted here before.