A new chapter for the Matrix Profile in R For a while, tsmp looked quiet. The project was not standing still, though: the Matrix Profile tools for R have been reorganized around a much clearer split between the computational engine and the everyday data-mining interface.
In tsmp 0.5.1, the package that many of us have used for years becomes a pure-R analytical layer. The new matrixprofiler package takes over the computationally intensive work in an optimized C++17 engine.
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1 What is a Time Series A set of observed values ordered in time, or we can say, repeated measurement of something usually with the same fixed interval of time (hourly, weekly, monthly).
A collection of observations made sequentially in time[1].
If the variable we are measuring is a count variable, we may have a Poisson Time Series (that is for later).
A time series \(T \in \mathbb{R}^n\) is a sequence of real-valued numbers \(t_i \in \mathbb{R} : T=[t_1,t_2,\dots,t_n]\) where \(n\) is the length of \(T\).
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It took some time, as you can see in the previous post, but the matrixprofiler package is done!
What does this mean?
The UCR Matrix Pofile is growing, undoubtedly, and the tsmp package is getting almost 700 downloads per month. We decided that we needed to separate the core from the practical usage of Matrix Profile.
So the matrixprofiler package has been born and is focused on having the low-level code (C/C++), for speed and robustness.
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