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I am very new to time series/panel/longituginal data. From my understanding Panel data = multi-object time series and I have some panel data in long format (objects have multiple rows corresponding to different time steps). The objective is to predict/classify if a person is going to leave the job due to factors of the job (hrs worked etc). The data looks something like this:

person month # of hrs worked Quit Other Feats
Mary 2 30 0
Mary 3 20 0
Mary 4 80 0
John 1 100 1
John 2 10 1
John 3 60 1
John 4 40 1
John 5 100 1
Cate 3 11 0
Cate 4 19 0
Cate 5 29 0
Cate 6 14 0
... ... ...

Where "Quit" is the y or target variable. I am curious how I can predict using this data if an existing person will quit and if a new person will quit. Can I just feed this into a random forest and expect it to do well? (similar to this) Do I need to transform it to wide format and keep 1 row per person where all the longitudinal aspects are in the columns? I have tried feeding this into a random forest and it gave a good accuracy and AUC but I am not sure if this is the correct way to solve this problem? I have seen people use RNNs to solve this.

Similarly, what about regressing/forecasting on this data? Say instead of a binary outcome I have dates of when a person quit, what techniques exist to regress on this kind of data? Can I just run a linear regression model on this data? What about pooled OLS or GEEs?

One last question is how is this different from classifying on time-series data instead? They both have a time component but panel data has multiple objects.

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