# What machine learning techniques to use to predict for multiple seperate sequences of time-series data?

I am having difficulty structuring my data and finding a machine learning technique to predict my outcome.

My data: I have a number of users with observations of a number of factors each year, each user having a separate 'time-series', and I want to determine the outcome for the next year.

User | Year | Factor1 | Factor2 | FactorN | Outcome 1 | 2015 | 1 | 2 | N | 1 1 | 2016 | 2 | 2 | N | 1 1 | 2017 | 3 | 4 | Y | 1 2 | 2015 | 3 | 4 | N | 1 2 | 2016 | 2 | 3 | Y | 0 2 | 2017 | 1 | 4 | Y | 1 3 | 2015 | 3 | 4 | N | 1 3 | 2016 | 2 | 3 | N | 0 3 | 2017 | 1 | 4 | Y | 0 

From my research I thought a recurrent neural network might be the right choice due to to its efficacy with sequences of data, but I'm unsure how to input the data I have into a RNN. I'm not even sure I'm tackling the problem correctly.

Is an RNN the right choice? Would it be more effective to 'flatten' the data into one row containing all years data or deltas between each year?