A Markov Chain Prediction Model Based on Rural Tourism Supply and Demand Matching Governance Model from the Perspective of Cultural Tourism Integration

Abstract

Exploring rural tourism supply and demand matching to promote high-quality rural tourism development. In this paper, we start from the Markov chain model, use the gray GM(1,1) model to divide the state of the Markov chain model and correct the relative error for the weighted Markov chain prediction. The corrected errors are used to construct the gray-weighted Markov chain model, and the arithmetic tests and example data analysis are conducted for the model. In terms of the model accuracy, it was improved by 12.75%, 9.28%, and 7.98% compared with the ARIMA model, ES model, and W-Markov model, respectively. From the perception of supply-demand matching, four demands are in low perception, and three demands are in high perception. This indicates that the use of the gray-Markov chain model can effectively realize the analysis of rural tourism supply and demand matching and also provides theoretical support for rural tourism to realize the supply and demand matching with tourists.

Keywords

• markov chain
• gray gm(1
• 1) model
• relative error correction
• data analysis
• supply and demand matching.
• 68t05