mmpp: A package for calculating similarity and distance metrics for simple and marked temporal point processes

Hideitsu Hino, Ken Takano, Noboru Murata

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    A simple temporal point process (SPP) is an important class of time series, where the sample realization of the process is solely composed of the times at which events occur. Particular examples of point process data are neuronal spike patterns or spike trains, and a large number of distance and similarity metrics for those data have been proposed. A marked point process (MPP) is an extension of a simple temporal point process, in which a certain vector valued mark is associated with each of the temporal points in the SPP. Analyses of MPPs are of practical importance because instances of MPPs include recordings of natural disasters such as earthquakes and tornadoes. In this paper, we introduce the R package mmpp, which implements a number of distance and similarity metrics for SPPs, and also extends those metrics for dealing with MPPs.

    Original languageEnglish
    Pages (from-to)237-248
    Number of pages12
    JournalR Journal
    Volume7
    Issue number2
    Publication statusPublished - 2015

    Fingerprint

    Tornadoes
    Distance Metric
    Point Process
    Disasters
    Time series
    Earthquakes
    Spike
    Metric
    Marked Point Process
    Disaster
    Earthquake
    Similarity
    Point process

    ASJC Scopus subject areas

    • Statistics and Probability
    • Numerical Analysis
    • Statistics, Probability and Uncertainty

    Cite this

    mmpp : A package for calculating similarity and distance metrics for simple and marked temporal point processes. / Hino, Hideitsu; Takano, Ken; Murata, Noboru.

    In: R Journal, Vol. 7, No. 2, 2015, p. 237-248.

    Research output: Contribution to journalArticle

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