Time Series Forecasting with Stochastic Markov Models Based on Fuzzy Set and Grey Theory

Abstract

The stochastic Markov model is combined with fuzzy set concept and grey system for improving forecasting performance. The data for model test is obtained from ACI including Hong Kong, Beijing, Taoyuan, Incheon and Narita international airport. The empirical results show that fuzzy Markov model has better predictive performance with the data with trend and intercept. For the data with random walk, grey Markov model performs better. The paper also examines the effects of transition state and length of interval on the forecasting performance with the result. Keywords: Markov model, Grey Markov model, Air cargo flow, ARIMA, MAPE.

Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

“…One hundred of the 167 data points (from February to May 2014) were used as the modeling data to build both the sARIMA model and the sARIMA-pf model. To better illustrate the effectiveness of the proposed method, the Grey model GM (1,1) [14] and the second-order exponential smoothing model [4] with a smoothing factor α = 0.15 were also trained with the same data. The rest of the data were used to test the performance of the models.…”

“…By combining the kernel extreme learning machine with the ARMA model, their methods showed better robustness compared with the methods using single models. Wong et al [14] combined the Markovian model with the Grey model and found that a fuzzy-Markovian model showed better performance on the observations with trends and intercepts. Normally, such hybrid methods show better performance under the certain circumstances, but the methods are also limited by the characteristics of the combined models.…”