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27 lines
1.7 KiB
27 lines
1.7 KiB
## Matrix Profiles Are Cool
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... and I've been interested in them going on a year now. The mathematics involved are deceptively
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simple, relying only on a z-normalized euclidean distance comparison between Fourier transformed
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subsequences. After wrapping my head around that primitive I dove into [the literature][1]. Much
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of the early academic literature regarding the MP is devoted to speeding up the calculation of
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Fourier transformations on a sliding window of subsequences and other novel improvements and
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shortcuts in the mathematics.
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This repository is a collection of code relating to the [stumpy tutorial][2]. Some of it is simply
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copied in as I follow along, I have tried to mark as clearly as possible where I make my own
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extrapolations. All data related to the tutorial is also mirrored in a data directory for each entry.
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## 1 - STUMPY Basics
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Above what was seen in the tutorial I started on a general purpose motif function which takes the dataset
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and a computed matrix profile and returns possibly multiple motif groups. It takes 2 threshold parameters,
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one for the absolute value of the matrix profile at the given point and the other for a percentage of the
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maximum data magnitude.
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### TODO
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I remember reading somewhere about an upper bound on matrix profile values. I should find that again
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and calculate a percentage of the upper bound rather than having mp_thresh be an absolute value.
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The Motifs class and function should be broken off into their own module for re-use elsewhere, including
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Jupyter.
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[1]: https://www.cs.ucr.edu/%7Eeamonn/MatrixProfile.html "Resources and papers on the Matrix Profile"
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[2]: https://stumpy.readthedocs.io/en/latest/tutorials.html "stumpy tutorial" |