# PSD of a point process with power-law inter-event times

In the previous post we have seen that introducing memory into the memory-less Poisson process makes things very complicated. But we haven't yet still seen 1/f noise. So now let us consider power-law distributed inter-event times!

## Pareto distributed inter-event times

Let us assume that inter-event times are distributed according to the Pareto distribution. To make the process stationary, let us impose limits on the possible of inter-event times: they must be larger or equal to \( \tau_{\mathrm{min}} \) and smaller or equal to \( \tau_{\mathrm{max}} \). Then the PDF of the bounded Pareto distribution can be written as:

\begin{equation} p\left(\tau\right)=\begin{cases} \frac{\alpha\tau_{\mathrm{min}}^{\alpha}}{1-\left(\frac{\tau_{\mathrm{min}}}{\tau_{\mathrm{max}}}\right)^{\alpha}}\cdot\frac{1}{\tau^{\alpha+1}} & \text{for }\tau_{\mathrm{min}}\leq\tau\leq\tau_{\mathrm{max}},\\ 0 & \text{otherwise}. \end{cases} \end{equation}

To use general result derived in an earlier post we need the expression for the characteristic function of this distribution. And it is given by:

\begin{equation} \chi_{\tau}\left(f\right)=\frac{\alpha\left(-2\pi\mathrm{i} f\tau_{\mathrm{min}}\tau_{\mathrm{max}}\right)^{\alpha}}{\tau_{\mathrm{max}}^{\alpha}-\tau_{\mathrm{min}}^{\alpha}}\cdot\left[\Gamma\left(-\alpha,-2\pi\mathrm{i} f\tau_{\mathrm{min}}\right)-\Gamma\left(-\alpha,-2\pi\mathrm{i} f\tau_{\mathrm{max}}\right)\right]. \end{equation}

In the above \( \Gamma\left(s,x\right) \) is the upper incomplete Gamma function. The expression above is not that helpful if we want to derive compact expression for the PSD. We need an approximation, and unlike in the previous post, this time it works! The approximation for \( \frac{1}{2\pi\tau_{\mathrm{max}}}\ll f\ll\frac{1}{2\pi\tau_{\mathrm{min}}} \) and \( 0 < \alpha < 2 \) (with an exception for particular case of \( \alpha = 1 \):

\begin{equation} \chi_{\tau}\left(f\right)\approx 1+\frac{\alpha}{\alpha-1}\cdot\left(2\pi\mathrm{i} f\tau_{\mathrm{min}}\right)-\Gamma\left(1-\alpha\right)\cdot\left(-2\pi\mathrm{i} f\tau_{\mathrm{min}}\right)^{\alpha}. \end{equation}

For the particular case of \( \alpha = 1 \), we have:

\begin{equation} \chi_{\tau}\left(f\right) \approx 1-\pi^{2}f\tau_{\mathrm{min}}+\left[1-C_{\gamma}-\ln\left(2\pi f\tau_{\mathrm{min}}\right)\right]\cdot\left(2\pi\mathrm{i} f\tau_{\mathrm{min}}\right). \end{equation}

In the above \( C_\gamma \) is the Euler's gamma constant (\( C_\gamma = 0.577\ldots \)).

## The power spectral density

So, from earlier we have that:

\begin{equation} S(f) = 2 \bar{\nu} \left( 1 + \mathrm{Re} \left[ \frac{2}{1 - \chi_\tau(f)} \right] \right) . \end{equation}

Thus, for the particular case of \( \alpha = 1 \), we obtain: \begin{equation} S(f) \propto \frac{1}{f \tau_{\mathrm{min}} ( \pi^2 + 4 [1 - C_\gamma - \ln(2\pi f \tau_{\mathrm{min}})]^2)} . \end{equation}

As long as \( \pi^2 \gg 4 [1 - C_\gamma - \ln(2\pi f \tau_{\mathrm{min}})]^2 \) it seems we can celebrate obtaining the illusive 1/f noise? But recall that we have assumed that both \( f \) and \( \tau_{\mathrm{min}} \) are small. Thus the natural logarithm in the expression above is a large negative number, at least certainly larger than \( \pi \). Thus the condition is not satisfied and we don't have straight \( 1/f \) dependence in the spectral density.

For the other cases, we do not obtain \( 1/f \) dependencies, but we get something reasonably close. For \( 0 < \alpha < 1 \):

\begin{equation} S(f) \propto \frac{1}{f^\alpha}. \end{equation}

While for \( 1 < \alpha < 2 \), we have:

\begin{equation} S(f) \propto \frac{1}{f^{2-\alpha}}. \end{equation}

So for \( \alpha \) not close to unity we have pure power-law dependence between the power spectral density and frequency. The exponents are always below \( 1 \), and thus closer to the white noise.

## Interactive app

Interactive app below samples time series from the point process with inter-event times being sampled from the bounded Pareto distribution. To verify the analytical intuitions above, we perform spectral analysis on the discretized time series. The discretized time series are obtained by counting the number of events inside unit time windows (the top plot). In total \( 2^{20} \) (roughly one million) such time windows are observed each time "Generate" button is pressed. PSD of the sampled series is shown in the bottom plot as a red curve, dark gray curves are best fits made according to the analytical predictions (the exponents of the frequency dependence remain the same, but the constant term is fitted according to the generated numerical data).