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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
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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.

enter image description here

I want to know/discuss what algorithms are appropriate (pros and cons, efficiency, optimization, etc) for such an analysis.

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1 Answer 1

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Check Darts for time series forecasting. In the README.md of their repository, you will find a table with some pros and cons of the implemented algorithms.

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  • $\begingroup$ @Oxedu thanks, I looked into it. Can you give me a working example of how to load my kind of data into it ? I am struggling with this, once loaded I can play with it $\endgroup$
    – Ayan Mitra
    Commented Mar 9, 2022 at 15:11

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