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I have a long time series signal. This signal is usually very stable, but it will change when the sensor is stimulated, and this change is usually very short. I know this can be trained using the labeled method(like neural network ,CNN, etc), but it takes a lot of time to label, this is because my change time is very short(about 4 seconds), and the change time is not much. So, I want to generate a number of signals similar to patterns using random numbers, and then use an autoencoder(or feature extraction method) to learn features before performing detection.

What I want to ask is if there are any errors in my ideas or can anyone provide some ideas or opinions. Many thank!

The pattern will not be obvious, but like this: enter image description here

My thoughts: enter image description here

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  • $\begingroup$ To get a better idea, can you post the actual data of any one sample? $\endgroup$ Jul 1, 2021 at 8:41

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If your signal follow simple square patterns like you've displayed, why not using a simpler solution? Some smoothing to reducte noise (ex: kalman filter) + a derivative function (ex: diff function in numpy) to detect ups and downs should be enough to detect the signal patterns, including their durations.

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A good package for finding 'similar' signals is the matrix profile for Python.

If I understand your question correctly, then it would spare you from creating random signals to train a classifier. Also, in the package you are able to give a window length, so assuming your signal is evenly sampled, then you can give the maximal duration and it should work. It is specifically designed for long signals and to my understanding is based on autocorrelation.

https://matrixprofile.docs.matrixprofile.org/

https://matrixprofile.docs.matrixprofile.org/examples/ECG_Annotation_Vectors.html

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Dynamic time warping (DTW) is a great way to find related chunks in different signals.

https://dynamictimewarping.github.io/

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