statistical quantity to estimate the oscillatory behavior of a stream of numbers
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I have a stream of numbers to process in order. I need to estimate the amount of oscillatory behaviour in the stream.
For example,
${-1.5, -0.2, 1.3, 0.0, -1.3, 0.1, 1.6, 0.2, -1.7, -0.1, 1.3}$ has a significant oscillatory behavior
${-1.7, -1.5, -1.3, -0.2, -0.1, 0.0, 0.1, 0.2, 1.3, 1.3, 1.6}$ has almost no oscillatory behavior
The above two samples are permutations of each other. Hence, most of (all of that I am aware of) statistical measures remain the same for the two samples.
I am looking for a statistical quantity which can nicely gauge the oscillatory behaviour of a number stream.
There aren't any more conditions on the requirements. This is for preprocessing a bulk of data for an SVM classifier; hence, as long as the statistical measure estimates it reasonably well, it's good.
statistics
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add a comment |
$begingroup$
I have a stream of numbers to process in order. I need to estimate the amount of oscillatory behaviour in the stream.
For example,
${-1.5, -0.2, 1.3, 0.0, -1.3, 0.1, 1.6, 0.2, -1.7, -0.1, 1.3}$ has a significant oscillatory behavior
${-1.7, -1.5, -1.3, -0.2, -0.1, 0.0, 0.1, 0.2, 1.3, 1.3, 1.6}$ has almost no oscillatory behavior
The above two samples are permutations of each other. Hence, most of (all of that I am aware of) statistical measures remain the same for the two samples.
I am looking for a statistical quantity which can nicely gauge the oscillatory behaviour of a number stream.
There aren't any more conditions on the requirements. This is for preprocessing a bulk of data for an SVM classifier; hence, as long as the statistical measure estimates it reasonably well, it's good.
statistics
$endgroup$
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Maybe look at certain components of the discrete Fourier transform?
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– Connor Harris
Dec 10 '18 at 14:56
add a comment |
$begingroup$
I have a stream of numbers to process in order. I need to estimate the amount of oscillatory behaviour in the stream.
For example,
${-1.5, -0.2, 1.3, 0.0, -1.3, 0.1, 1.6, 0.2, -1.7, -0.1, 1.3}$ has a significant oscillatory behavior
${-1.7, -1.5, -1.3, -0.2, -0.1, 0.0, 0.1, 0.2, 1.3, 1.3, 1.6}$ has almost no oscillatory behavior
The above two samples are permutations of each other. Hence, most of (all of that I am aware of) statistical measures remain the same for the two samples.
I am looking for a statistical quantity which can nicely gauge the oscillatory behaviour of a number stream.
There aren't any more conditions on the requirements. This is for preprocessing a bulk of data for an SVM classifier; hence, as long as the statistical measure estimates it reasonably well, it's good.
statistics
$endgroup$
I have a stream of numbers to process in order. I need to estimate the amount of oscillatory behaviour in the stream.
For example,
${-1.5, -0.2, 1.3, 0.0, -1.3, 0.1, 1.6, 0.2, -1.7, -0.1, 1.3}$ has a significant oscillatory behavior
${-1.7, -1.5, -1.3, -0.2, -0.1, 0.0, 0.1, 0.2, 1.3, 1.3, 1.6}$ has almost no oscillatory behavior
The above two samples are permutations of each other. Hence, most of (all of that I am aware of) statistical measures remain the same for the two samples.
I am looking for a statistical quantity which can nicely gauge the oscillatory behaviour of a number stream.
There aren't any more conditions on the requirements. This is for preprocessing a bulk of data for an SVM classifier; hence, as long as the statistical measure estimates it reasonably well, it's good.
statistics
statistics
asked Dec 10 '18 at 12:39
YashasYashas
172115
172115
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Maybe look at certain components of the discrete Fourier transform?
$endgroup$
– Connor Harris
Dec 10 '18 at 14:56
add a comment |
$begingroup$
Maybe look at certain components of the discrete Fourier transform?
$endgroup$
– Connor Harris
Dec 10 '18 at 14:56
$begingroup$
Maybe look at certain components of the discrete Fourier transform?
$endgroup$
– Connor Harris
Dec 10 '18 at 14:56
$begingroup$
Maybe look at certain components of the discrete Fourier transform?
$endgroup$
– Connor Harris
Dec 10 '18 at 14:56
add a comment |
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$begingroup$
Maybe look at certain components of the discrete Fourier transform?
$endgroup$
– Connor Harris
Dec 10 '18 at 14:56