A dynamic Markov model for nth-order movement prediction

Ian Cornelius, James Shuttleworth, Sandy Taramonli

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    1 Citation (Scopus)


    Prediction of the location and movement of objects is a problem that has seen many solutions put forward based on Markov models. The usual method involves the use of historical data for building a stochastic model in order to make future predictions. Here we present a method for predicting movement of an object using a Markov Model that is not populated by historical data from previous experiments. The proposed method introduces a novel mechanism that dynamically updates the transition probability matrix through analysis of stochastic properties of the data as it is collected. The model gives high accuracy predictions on an object's immediate next movement using a range of orders with results ranging from 79% to 96% dependent upon the type of movement exhibited by the object and order of the model.
    Original languageEnglish
    Title of host publication2017 4th International Conference on Systems and Informatics (ICSAI)
    Number of pages7
    ISBN (Electronic)978-1-5386-1107-4, 978-1-5386-1106-7
    ISBN (Print) 978-1-5386-1108-1
    Publication statusPublished - Nov 2017
    EventInternational Conference on Systems and Informatics - hangzhou, China
    Duration: 11 Nov 201713 Nov 2017


    ConferenceInternational Conference on Systems and Informatics
    Abbreviated titleICSAI
    Internet address


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