A High-Order Model for Forecasting Rice Production Based on Combined Fuzzy Time Series and Particle Swarm Optimization

Nghiem Van Tinh

Abstract


Crop production is considered as one of the real world complex problem due to its non-deterministic nature and uncertain behavior. Particularly, forecasting of rice production for a lead year is pre-eminent for crop planning, agro based resource utilization and overall management of rice production. As such, main challenge in rice production forecasting is to generate realistic method that must be capable for handling complex time series data and generating forecasting with almost tiny error. However, first-order fuzzy time series models have proven to be insufficient for solving these problems for the best forecasting accuracy. For this reason, this paper presents a novel high-order model based on fuzzy time series (FTS) and particle swarm optimization (PSO) which overcomes the drawback mentioned above. First, the global information of fuzzy logical relationships is combined with the local information of latest fuzzy fluctuation to find the forecasting value in the defuzzification stage. Second, the particle swarm optimization technique is developed to adjust the lengths of intervals in the universe of discourse for the fuzzification stage. To illustrate the forecasting process and the effectiveness of the proposed model, two numerical datasets of average rice production of Viet Nam and enrolment of students of Alabama University are examined. The examined results show that the proposed model gets lower forecasting errors than those of other existing models

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References


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DOI: https://doi.org/10.23956/ijarcsse.v8i8.836

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