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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:

  1. It's a tedious process to recalculate everything on each iteration, if the historic data is large then this would take too long.
  2. 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?

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I think you should consider time series modeling instead of observation based classification models. In the letter, you are propagating error in each prediction year.

I would use ARIMA, LSTMs, maybe semi-supervised models and motif discovery techniques.

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  • $\begingroup$ How can I incorporate in ARIMA all the additional features of the model? $\endgroup$ – ps0604 Aug 15 at 19:10

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