Enhanced forecasting of rice price and production in Malaysia using novel multivariate fuzzy time series models

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.