Forecasting the public financial budget expenditure in Dongguan with an optimal weighted combination Markov model

Abstract

In this work, a novel optimal weighted combination Markov model (OWCMM) is proposed to forecast the public financial budget expenditure of Dongguan, China, from 2016 to 2020. The new model is constructed based on the optimal combination, which includes the fractional grey model, the Fourier function regression model and the autoregressive integrated moving average model (ARIMA), and modifies this optimal combination by the Markov model. The number of the optimal fractional order is determined by particle swarm optimization algorithm. One example is provided to verify the high fitting accuracy of the new model, the results show that the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the optimal weighted combination Markov model are smaller than that of the quadratic function model (QFM), the classical combinatorial model and its three sub-models, which proves the robustness of the optimal weighted combination Markov model. This work will provide a scientific basis and technical reference for the further research in finance field.

Keywords

• optimal weighted combination markov model
• public finance budget expenditure
• particle swarm optimization algorithm
• mape
• rmse