I have time series like 500 data points of $(x,y)$ pairs, where $x$ = time in seconds and $y$ = signals. Each of these candidates/time series has an additional label, which tells about the nature of the wave source. In this way, I have about a thousand candidates. Now I want to do a forecast analysis, such that the label can be predicted (after supervised learning). Typical data looks like this:
t1,t2,t3,...,t500, sa1,sa2,sa3,...,sa500,Label1 t1,t2,t3,...,t500, sb1,sb2,sb3,...,sb500,Label2 t1,t2,t3,...,t500, sc1,sc2,sc3,...,sc500,Label3 . . . t1,t2,t3,...,t500, sx1,sx2,sx3,...,sx500,LabelX
The time array(/values) is the same for all candidates. A typical plot of three random candidates look is shown below.
I want to know/discuss what algorithms are appropriate (pros and cons, efficiency, optimization, etc) for such an analysis.