It is good to conjecture. However, there are calculations that will put more meat on the bone:
1. Transform all variables to zero-mean/unit-variance
2. Simultaneous plots of all inputs and targets
3. Autocorrelation plots of targets with significant values highlighted
4. Crosscorrelation plots of targets and inputs with significant values highlighted
5. Plots of SOME targets vs SOME inputs
6. Combining all three models (Timedelaynet, Narnet and Narxnet) for post target prediction:
For example, using Narnet on BOTH input and target can yield post target predictions of both input and output.
The predicted input can then be used with Timedelaynet and Narxnet to obtain more predictions of the output.
If the original data is error-free and stationary, success will depend, primarily, on the accumulation of errors because effective combinations of lags and hidden nodes can be obtained via trial and error.
NOTE: IN CONTRADICTION TO PREVIOUS POSTS, POST-TARGET NARXNET PREDICTIONS CANNOT BE ADEQUATELY APPROXIMATED WHEN EMPTY CELLS ARE SUBSTITUTED FOR THE POST-TARGET INPUT!
Hope this helps.