  Markov chain model is widely applied in many fields, especially the field of
prediction. The classical Discrete-time Markov chain(DTMC) is a widely used
method for prediction. However, the classical DTMC model has some limitation
when the system is complex with uncertain information or state space is not
discrete. To address it, a new belief Markov chain model is proposed by
combining Dempster-Shafer evidence theory with Markov chain. In our model, the
uncertain data is allowed to be handle in the form of interval number and the
basic probability assignment(BPA) is generated based on the distance between
interval numbers. The new belief Markov chain model overcomes the shortcomings
of classical Markov chain and has an efficient ability in dealing with
uncertain information. Moreover, an example of inventory prediction and the
comparison between our model and classical DTMC model can show the
effectiveness and rationality of our proposed model.
