Say I have a data set such as the following:person, Time, Value, Eventperson1, 2010-07-02 00:00:00, 5.4, 0person2, 2010-07-02 10:00:00, 12.7, 0We have a current model in place at work that doesn't take into account the temporal aspect of our data. In that implementation, the model was trained with only unique values for 'person', and it throws away the time variable. However, it has come to our attention that we can look at our data as a sequence instead. This starting time is unique for each person, and clearly associated with only that person, so merely pretending each person is independent and just treating each row as an individual data point wouldnt make any sense. The following is what I've restructured the data as:person, Time, Value, Eventperson1, 2010-07-02 00:00:00, 5.4, 0person1, 2010-07-02 00:00:15, 3.6, 0person1, 2010-07-02 00:00:30, 2.4, 0person2, 2010-07-02 10:00:00, 12.7, 0person2, 2010-07-02 10:01:15, 12.8, 0person2, 2010-07-02 10:01:30, 13.1, 1This sequence for each person would continue until and 'event' or 'non-event'. I'm totally unfamiliar with machine learning on time series data. All of the examples I've read with different models treat the data as one big sequence corresponding to one entity, while our data clearly doesn't work like that. Is the way I've structured the data the right way to approach a time series model? And if so, what would be an appropriate model to consider?
Learning with groups of sequential data
machine learning;time series
The data that you are showing is typical survival data. If you want to model the event depending on time and value you should look into survival models with time dependent covariates. If you want to do this without any assumptions on the distribution you can start with a Kaplan Meier estimate as explained here. If you want to use parametric models you can look at Weibull or Gamma regression. If you are new to this topic I can highly recommend to browse the examples of the packages in the CRAN Survival Task View.