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I am currently working on a time series data and I would like to quantify how volatile it is.

Here volatile I mean how "shaky" the series is.

If the series is smooth than it is not volatile.

I have an idea to solve this problem, but it is kind of inconvenient.

The idea is to first do a regression/smoothing on the series. Then compute the sum of squared error between the smoothed series and the original series.

Any other better idea or and references suggest me to have a look?

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    How will you do the regression/smoothing. If you use a polynomial of arbitrarily high degree, you can get a smooth curve which is exactly as "volatile" as your time-series.2012-07-25
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    The over fitting/training is also a problem. I am thinking to use spline but I still need to read more about it.2012-07-25
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    BTW: [volatility in finance](http://en.wikipedia.org/wiki/Volatility_(finance)) is more or less [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation).2012-07-25

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