Problem : There are several events (eventA, eventB,....) represented by waveforms. For each event there are several csv files (eventA1.csv, eventA2.csv,...eventAn.csv) having the points(x,y) from which the corresponding waveform can be generated.
Using these waveforms as training data, waveform segments on (test data) larger data should be identified i.e. the interval during which an event occurs is needed to be labelled on the test data (See image for reference).
- What would be the best strategy, tools and algorithms to address this problem?
If classification model is to be generated then,
- The points are just to describe the nature of the waveform i.e. the test waveform will not be on same points but with similar nature e.g. the concavity, convexity, slope etc.
Different events in training graphs are represented using different number of points i.e. event A can be determined by 20 points, while event B has 200 points.
- How do I create feature vectors to include all these things for classification?
- How would the test data be analysed, as it has large number of points on the large waveform i.e. how to segment it to generate features and give it to classification model?