Pattern Recognition within numerical data

by Nikaidoh   Last Updated June 12, 2017 12:19 PM

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)?

Related Questions

Machine learning for traffic sign recognition

Updated September 09, 2017 20:19 PM

Any idea about application of deep dream?

Updated August 12, 2015 19:08 PM