Affiliations: [a] Department of Culinary Arts, National Kaohsiung University of Hospitality and Tourism, Kaohsiung, Taiwan
| [b] Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| [c] Management School, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Correspondence:
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Corresponding authors. Yenming J. Chen, Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. E-mail: [email protected] and Albert Jing-Fuh Yang, Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. E-mail: [email protected].
Abstract: A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.
Keywords: Distribution entropy, spatial diffusion, stochastic automata (SA), evolutionary game (EG), machine learning
