# Neural Network Timeseries Modeling with Predictor Variables

Many have shown the effectiveness of using neural networks for modeling time series data, and described the transformations required and limitations of such an approach. R's forecast package even implements one approach to this in the nnetar function.

Based on my reading, all of these approaches are for modeling a single outcome variable based on its past observations, but I'm having trouble finding a description of a neural-network-based approach that also incorporates independent predictor variables (a sort of ARIMAx analogue for neural networks). I've found references to Nonlinear autoregressive exogenous models (NARX), which seem like they should be what I'm looking for, but all the reading I've been able to find talks more about using this approach for multi-step-ahead prediction of a univariate series.

Can anyone point me in the right direction on this? For bonus points, does anyone know of an implementation of what I'm looking for in R?

• The nnetar function allows independent predictor variables. Read the help file. Jul 11 '16 at 8:20
• @RobHyndman: Thanks! I must have missed that. I will revisit it! Jul 11 '16 at 11:49
• @RobHyndman: It seems I was using v. 6.2, but this feature is in newer versions. Thanks for the direction, I'm a big fan of your work! Jul 11 '16 at 17:52