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My objective is to predict the sales 6 weeks in advance. I have data that from 01-Jan-2013 to 31-June-2015. I am supposed to predict the sales from 01-Aug-2015 to 17-Sept-2015 using machine learning. I was trying to develop a method to cross validate. this is the way I split the data:

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|        |  training          |     Testing     |
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|  fold  |  year  |  months  |  year  |  months | 
|  1     |  2013  |  4,5,6   |  2013  |  7,8    |
|  2     |  2013  |  9,10,11 |  2014  |  12,1   |
|  3     |  2014  |  2,3,4   |  2014  |  5,6    |
|  4     |  2014  |  7,8,9   |  2014  |  10,11  |
|  5     |  2015  |  12,1,2  |  2015  |  3,4    |
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Is this the proper way to do it?

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First, you confuse test set with cross-validation set. Test set is used to measure performance of a model AFTER you select your best model based on different model performance, measured on cross-validation set. And about splitting your data I would suggest the following:

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|        |  training          |     Testing     |
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|  fold  |  year  |  months  |  year  |  months | 
|  1     |  2013  |  4,5,6   |  2013  |  7,8    |
|  2     |  2013  |  7,8,9   |  2014  |  10,11  |
|  3     |  2014  |  10,11   |  2014  |  12,13  |
 -----------------------------------------------

The idea is to use monthly data both as input and output (target value) since that way you will have more data to train your model.

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