A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

Abstract

Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1) model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1) model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem-foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.

Forecasting the Power Generation Mix in Italy Based on Grey Markov Models

“…Additionally, the enhanced MCGM(1, 1) approach, merging the principles of the GM(1, 1) model with those of the Markov chain, delivers improved statistical assessments for datasets that demonstrate significant fluctuations. The Grey Markov Model does not require restrictive assumptions concerning the relationships between variables and data stationarity [25]. However, for reasonable outcomes, its deterministic component should be able to explain the data trend, while the Markov chain should enhance the deterministic predictions.…”

Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam

“…The forecasting models and their extensions have been applied in the real world to overcome the complexity and uncertainty [36][37][38][39]. Grey theory is primarily concerned with ambiguous system models in which there is inadequate information to perform system link analysis, model creation, predictions, and judgments.…”

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

“…Although a large variety types of grey prediction models can be studied, the most widely used grey prediction model is a GM(1,1) model because of its high computational efficiency [32]. So far, the GM(1,1) model has been widely applied to a broad spectrum of fields, including economics [33,34], environment [35], tourism [36,37], industry [38,39], education [40], transportation [41] and energy [29,[42][43][44][45]. Wang et al [33] presented an improved grey model, named PRGM(1,1), to forecast tertiary industry in Beijing City in China.…”

“…Li et al [35] proposed an improved GM(1,1) model to predict the aquaculture water quality in Yixing City and Dongying City in China. Hu et al [37] applied MCGM(1,1) model to forecast the number of foreign tourists from eight main countries. Li et al [38] used the GM(1,1) model to forecast automobile production in Japan.…”