# Time Series Data Multi-Class Classification

This is a very general question, as I'm still very much in the learning phase with machine learning. I have some utility data around problematic meters. Even tho the data is "time series", I believe I can perform a multi-class classification (looking at 3 labels) towards the data, but would like some opinions before I pursue down that road.

I have been doing some feature engineering to derive other data points to help with the classification process (examples below are columns "Error1" and "Error2").

The meters come in 2 classes, those that are estimated issues ="1", and does that are non-estimated issues ="0".

My dataset roughly looks like below (I have several other Error features):

 Estimated     Meter ID          Date             DaysInDuration    Error1  Error2
0            BBA         11/19/2019               31              0       0
0            BBA         12/19/2019               62              1       0
0            BBA         12/19/2019               92              1       0
1            JJL         11/2/2019               120              1       0
1            JJL         12/2/20019              150              1       1
1            JJL         1/20/2020               180              2       2


What I would like to attempt is to use a classification model that can handle multi-class classification (possibly a decision tree), and produce a output such as below:

 Estimated     Meter ID          Date             DaysInDuration    Error1  Error2   Classification Label
0            BBA         11/19/2019               31              0       0            1
0            BBA         12/19/2019               62              1       0            1
0            BBA         12/19/2019               92              1       0            2
1            BBA         11/2/2019               120              1       0            3
1            JJL         12/2/2020                30              1       1            1
1            JJL         1/20/2020                60              2       2            1

Labels Meaning = "1" = low risk issue/ "2" = medium risk issue/ "3" = high risk issue


The model would classify the either "1","2", or "3" depending on the length of days the meter has been in the "DaysInDuration" column, and the number of counted errors in the "Error1" and "Error2" columns.

In my thoughts it feels like classification would still work, including with train test splits, as the classification is moreso from other data points versus the actual order dependency in a typical time series problem.