Bilal Muhammad, Alrasheedi Masad A, Aamir Muhammad, Abdullah Saleem, Norrulashikin Siti Mariam, Rezaiy Reza
Department of Statistics, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan.
Department of Mathematical Sciences, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, 87300, Pakistan.
Sci Rep. 2024 Dec 2;14(1):29903. doi: 10.1038/s41598-024-77907-4.
A significant portion of the world's population relies on rice as a primary source of nutrition. In Malaysia, rice production began in the early 1960s, which led to the cultivation of the country's most significant food crop up till the present day. Research on various aspects of the price and production of rice has been done by various methods in the past. In this study, we have adopted novel multivariate fuzzy time series models (MFTS) i.e. fuzzy vector autoregressive models (FVAR) alongside conventional vector autoregressive model (VAR) for assessing rice price and production using a dataset from the Malaysian Agricultural Research and Development Institute (MERDI). The proposed method(s) especially with the usage of Trapezoidal Fuzzy Numbers (TrFNs) have commendable accuracy with great future forecasts over the VAR model. The model selection was made by the least MAPE with the corresponding highest Relative Efficiency as criteria. The study fills the gap in applying advanced fuzzy models for rice forecasting, aiming to improve accuracy using fuzzy vector autoregressive (FVAR) models with Triangular Fuzzy Numbers (TFNs) and Trapezoidal Fuzzy Numbers (TrFNs) over traditional VAR models. The study's findings imply that the enhanced forecasting accuracy of FVAR models with Trapezoidal Fuzzy Numbers (TrFNs) can significantly assist local farmers and stakeholders in making informed decisions about production and pricing. This improved forecasting capability is expected to promote business growth within the Malaysian market and facilitate increased rice exports, ultimately contributing to the country's economic prosperity.
世界上很大一部分人口依赖大米作为主要营养来源。在马来西亚，水稻生产始于20世纪60年代初，从那时起，水稻一直是该国最重要的粮食作物。过去，人们通过各种方法对水稻价格和产量的各个方面进行了研究。在本研究中，我们采用了新颖的多元模糊时间序列模型（MFTS），即模糊向量自回归模型（FVAR），并结合传统向量自回归模型（VAR），利用马来西亚农业研究与发展研究所（MERDI）的数据集来评估水稻价格和产量。所提出的方法，特别是使用梯形模糊数（TrFNs）的方法，具有值得称赞的准确性，并且对VAR模型具有出色的未来预测能力。模型选择以最小平均绝对百分比误差（MAPE）和相应的最高相对效率为标准。该研究填补了在水稻预测中应用先进模糊模型的空白，旨在通过使用三角模糊数（TFN）和梯形模糊数（TrFN）的模糊向量自回归（FVAR）模型来提高预测精度，优于传统的VAR模型。该研究结果表明，具有梯形模糊数（TrFNs）的FVAR模型提高的预测准确性可以显著帮助当地农民和利益相关者就生产和定价做出明智的决策。这种改进的预测能力有望促进马来西亚市场内的商业增长，并促进水稻出口增加，最终为国家的经济繁荣做出贡献。
