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I have a 10GB file of a time series 1D signal. I want to find some patterns within this signal, I know CNN's are great for this but the problem is I don't have any training data.

Now, I could, of course, spend an entire week slowly making 100 versions of a certain pattern to train the CNN with. But maybe there is some other way?

Maybe there is a way for the neural network to work out patterns on its own and simply categorize them? Like this is pattern A, this is pattern B.

My ultimate goal is to look at any size data and find the occurrences of patterns within the data.

Does anyone have any idea how this problem could be solved? I am just starting with machine learning, so I am slowly learning what's possible in this field.

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You should look into unsupervised learning, which is machine learning without a human-labeled training set.

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  • $\begingroup$ Given that this answer is very short, maybe you should provide some examples of UL algorithms that could be useful for the particular problem the OP was trying to solve. $\endgroup$ – nbro Dec 2 '20 at 9:10

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