I got different input data\instances and for each of them correspond different sequences of numerical data, which I normalized for comparison.
For example, instance1 has:
seq1: 1.3, 2.4, 1.0, 1.25 ... seq2: 5.1, 3.9, 1.2, 7.8 ...
I normalized the sequences in different ways, for comparison. For example I got a normalization related with the starting value, or normalization intra values etc
I need to understand, to learn, pattern (if they exists) in these numerical sequences. For example, a trivial pattern would be: if my seq1 has a growing slope, it is likely that the next value will be greater than the previous one.
For this task I thought that a good approach would be the use of machine learning. For example using clustering, random forest, Decision Trees, or deep learning.
If I want to use algorithms like clustering, I need to specify the window on which I define my instance for the classifier, to learn the pattern. But in these way I cannot find pattern arbitrarily long.
What would you suggest? Is there a way to adapt solution like Convolutional NN to problem like this, to automatically extract pattern in the data (of arbitrarily long sequence)?