Interpreting results

Fundamentals and notation

The measured scaling of the time-series process is \(\delta\).

The \(\mu\) is a complexity index, defined as the power for the inter-event time distribution, \(\frac{1}{\tau^\mu}\), where \(\tau\) is inter-event time.

Frames of reference

For a totally random process, DEA yields \(\delta = 0.5\).

The closer \(\delta\) is to 1, and the closer \(\mu\) is to 2, the more complex the data-series is. Those are the critical values of \(\delta\) and \(\mu\).

If \(\delta < 0.5\) this usually means the time-series is not complex in the way DEA is designed to quantify.

Calculating mu

Two rules of calculating \(\mu\) are supported:

• \(\mu = 1 + \delta\), holds when \(1 < \mu < 2\)
• \(\mu = 1 + \frac{1}{\delta}\), holds when \(2 < \mu < 3\)

The correct rule for any particular analysis may vary. Because rigorous rules for determining which is correct in what situation have yet to be laid down, both candidates are calculated and plotted so that a user can compare them. Typically the correct value for will lie along the line, as the line represents the theoretical relationship. If you already have an expectation for what rule \(\mu\) should follow, e.g., from theoretical arguments, use that.

The theoretical justifications for the two methods of deriving from the scaling are given in Section 3.2 of Scafetta, N., & Grigolini, P. (2002) and Section 3.1-3.2 of Grigolini, P., Palatella, L., & Raffaelli, G. (2001).