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Social, technological and economic time series are divided by events whichare usually assumed to be random albeit with some hierarchical structure. It iswell known that the interevent statistics observed in these contexts differsfrom the Poissonian profile by being long-tailed distributed with resting andactive periods interwoven. Understanding mechanisms generating consistentstatistics have therefore become a central issue. The approach we present istaken from the Continuous Time Random Walk formalism and represents ananalytical alternative to models of non-trivial priority that have beenrecently proposed. Our analysis also goes one step further by looking at themultifractal structure of the interevent times of human decisions. We hereanalyze the inter-transaction time intervals of several financial markets. Weobserve that empirical data describes a subtle multifractal behavior. Our modelexplains this structure by taking the pausing-time density in the form of asuperstatistics where the integral kernel quantifies the heterogeneous natureof the executed tasks. An stretched exponential kernel provides a multifractalprofile valid for a certain limited range. A suggested heuristic analyticalprofile is capable of covering a broader region.

A model for interevent times with long tails and multifractality in human communications: An application to financial trading