Timestamp can be inclusive of "seconds" too, but the data may or may not change every second. it depends on the previous values(rows i.e data which came earlier w.r.t time).
Column1, Column2 .... ColumnN correspond to the variables (they change over time) and "Label" shows the different samples. You can assume that the values tend to decrease over time for a particular label.
Labels A1,B1,C1.........A2,... M labels.
Note : Values of timeNew of a Label depends on values of timeOld of that Label and Labels belong to its cluster.
I need to group Labels with similar behavior over time together (e.g. Label A1 and Label C1 should be put in the same cluster and B1,D2 may fall into same cluster over time as they tend to behave similar over time).
I thought of using DTW and get the similarity of each Label with respect to other Labels. but not sure, how to proceed when i have N Columns.
To be precise, i need to group Labels based on their similarities (Column1 .. ColumnN) over time and group them.
Once i group them when new data comes in i should be able to predict the values(Column1.. ColumnN) for a Label based on the previously seen data(can be just minutes closer to the current prediction) and the values associated with the Labels in its cluster and predict it accordingly.