Interwa

Interwa is a term used in theoretical and applied contexts to denote a framework for analyzing and representing the intervals between sequential events. It provides a generic language for discussing timing relationships and dependencies in time-ordered data.

Core concepts. An interva is the duration between two consecutive events in a sequence. An interva set comprises all intervals observed in a dataset, possibly partitioned by context such as channel, location, or modality. Representations include interval lists, histograms of interval lengths, and graphs that connect events with edges weighted by the interval duration.

Applications. In scheduling and operations research, interva models help optimize task timing and resource allocation by analyzing gaps between activities. In neuroscience, interva analysis characterizes inter-spike interval distributions to study neural coding. In finance, interva methods may be used to study times between trades or quotes to understand liquidity and clustering.

Methods. Data collection of event times, computation of intervas, and model fitting are central activities. Common approaches include parametric fits (exponential, gamma) and nonparametric estimators, with visualization and diagnostic checks. Handling irregular sampling, censoring, and missing events is an important consideration.

Relation to other concepts. Interva relates to interarrival time in renewal processes, inter-event time in point processes, and timing analysis in time-series. It complements existing tools for sequence analysis by focusing on timing gaps rather than sequence identities.

See also. Interarrival time, inter-event time, Hawkes process.