Guys,
I need your suggestion about how to go on.
I have 10 years of Total Agricultural Tractors sales Volumes. I have just 10 numbers (full years)
I need to forecast this year.
I thinking to find 10 years variables in internet like (GDP, Commodity prices, Wheater info, Subsidies and so on.
The variables could be:

Numerical ones like commodity price, GDP

Nominal ones like Weather (dryness, …), Subsidies (YES or NO)

Then based on this year variables forecast I want to find this year Total sales volumes.

There could be several ways to do this. We recently had two discussions about regression predictions with historical data. You could check them out and use the accompanying workflows as an example:

Question is do you want a correlation or a prediction. For correlation there is a node that would give you a matrix, prediction you could follow the last example.

You can see which variables have the strongest correlation with your Target, which would be Wheat, GDP with positive and “Agriculture, forestry, and fishing, value added (% of GDP)” with negative correlation (the larger your target the lower this value).

Great
I would like to predict 2020 value target based on p-value/correlation index of variable like GDP.
I have already 2020 GDP (and other variables…) forecast
Does is make sense?
Thanks
D.

Well you could try to do that. In this example I have used the data from 2015 and 2016 as test and the rest as training. With the model and the data for 2018 you could also predict the target. You just have to be careful what your target means. If it is the sales from 2018 the question would be when would you know the Target and when the other data. So in oder to make an actual prediction you would need the explaining variables before you would need the prediction (Target).

I the example workflow the best model is H2O GLM. Although the prediction for 2015 is not very good.

Row0 is 2015 and Row1 is 2016 (H2O omitted the RowIDs). 2016 is OK, 2015 not so much.

And as has been mentioned in the other discussions I referenced. With such few data points and a complicated market it is not so easy to make an accurate prediction. Some additional data might help. Or you could eliminate non-standard years (if there is such a thing).