I want to predict the trend values of a time serie [Y] based on the effect of other 10 input variables that can also have interaction. Since the combination of interaction is unknown, I am applying a regression NN to automatically detect and take that into account. The process I follow is:

Get raw data of [Y] time serie per week

Perfom decomposition and extract the [Y] Trend element per week

Merge observations of the [Y] Trend with the observations of the 10 variables by week into a common table

Normalize (min-max) all variables between 0 and 1

Fit a NN model with Trend as 1 output node and the 10 variables as the input nodes.

Perform k-fold cross validation

Am I following a logic approach or is it anything important that I'm missing?

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