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


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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