Affiliations: [a] Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| [b] Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
| [c] Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| [d] Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| [e] Department of Computer Science, National Pingtung University, Pingtung, Taiwan
Abstract: This study proposes a framework implementing triangular estimation for better modeling and forecasting time series. In order to improve the performance of estimation, we employ two sources of triangulation to generate a time series, which is statistically indistinguishable with the latent time series hidden in a system. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history, the framework has been validated by a large amount of records in vegetable auctions. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive bootstrapping of data. Our results demonstrate excellent estimation performance in discovering hidden states.
Keywords: Generative estimation, time series forecasting, triangulation data assimilation
