Time-varying transition probability matrix estimation and its application to brand share analysis

Tomoaki Chiba, Hideitsu Hino, Shotaro Akaho, Noboru Murata

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.

    Original languageEnglish
    Article numbere0169981
    JournalPLoS One
    Volume12
    Issue number1
    DOIs
    Publication statusPublished - 2017 Jan 1

      Fingerprint

    ASJC Scopus subject areas

    • Medicine(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Agricultural and Biological Sciences(all)

    Cite this