A new belief Markov chain model and its application in inventory prediction

TL;DR

This paper introduces a belief Markov chain model that integrates Dempster-Shafer theory to better handle uncertainty in complex prediction systems, demonstrated through inventory prediction.

Contribution

A novel belief Markov chain model combining Dempster-Shafer evidence theory with Markov chains to improve uncertain data handling in predictions.

Findings

The new model effectively manages uncertain information.

It outperforms classical DTMC in inventory prediction accuracy.

The model demonstrates rationality and efficiency in practical applications.

Abstract

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.