Let's say I have a time series $ \{t_i, x_i\} $ where the $x_i$ are strictly increasing and the $t_i$ are not regularly spaced. I can define a differential operator as

$$ \Delta_i = \frac{x_i-x_{i-1}}{t_i-t_{i-1}} $$

but that is very noisy.

Müller et. alia define a different operator with characteristic time $\tau$ in terms of iterated EMAs $\epsilon^n_T$ of levels $n=1,2,4$ as

$$ \Delta_\tau = \gamma(\epsilon^1_{\alpha\tau} + \epsilon^2_{\alpha\tau} - 2\epsilon^4_{\alpha \beta \tau}) $$

where $ \gamma=1.22208,\beta=0.65, \alpha^{-1}=\gamma(8\beta-3)$.

This operator has a good-looking kernel with a nice small tail, but for monotonically increasing series $x$ it is not strictly positive. What differential operator works well but lacks this sign problem? Should I just be smoothing the straight differentials?


Let's say I am tracking how frequently a stock has been mentioned in the news in the last 5 years. Some days there are no mentions, while on other days they are bountiful. The rate of mention is of course the derivative of the number of total times the stock has been mentioned.

I could just smooth the mention counts within a window and be done, but it seems elegant to apply a differential operator to the mention count. Unfortunately, as I noticed, I get some negative value which are clearly silly.

  • $\begingroup$ It might help to define what your goal is. What would a "good" kernel look like? Do you have examples of any data that you are trying to analyze? $\endgroup$ – Jason R Oct 28 '11 at 13:46
  • $\begingroup$ Thanks I added an example. A "good" kernel to me has a quick falloff like the one in Mueller. $\endgroup$ – Brian B Oct 31 '11 at 19:04
  • $\begingroup$ Just curious if you've happened upon an answer from somewhere else yet. I'm always interested to see overlap with dsp.SE and quantitative finance, which is a fascinating field. $\endgroup$ – Jason R Dec 1 '11 at 16:29
  • $\begingroup$ I never found anything so I just smooth the differentials for now. $\endgroup$ – Brian B Mar 7 '12 at 17:57
  • $\begingroup$ Your example sounds like a General Poisson process, probably nonhomogeneous. If not, please elaborate. $\endgroup$ – user28715 Jun 10 '17 at 2:43

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