I need to estimate the GDP % of a country three years into the future, based on historic data.
I have 30+ years of the following monthly data that includes features such as inflation and unemployment rate:
Year Month Inflation Unemployment % Other Features GDP % 1990 1 1.1 6.2 .... 2.3 1990 2 1.3 6.1 .... 2.4 ... ... ... 2019 6 0.8 4.8 .... 3.1 2019 7 0.9 4.9 .... 3.3
Using Random Forests (Python's scikit learn library), I can use the data to calculate the GDP of next month, 2019/8:
Once calculated (let's say GDP % = 3.2) I use this value and rerun the entire Random Forest process, including in the history data the value I obtained for 2019/8.
I continue iterating until I calculate 2022/8, and that is my result.
I have two issues:
- It's a tedious process to recalculate everything on each iteration, if the historic data is large then this would take too long.
- The 2022/8 result is obtained with just one path, I would need to rerun the overall process N times and take the average to have a more precise number.
I need this to work with machine learning (not necessarily Random Forests), any ideas how to improve/change this process?