Fuzzy Time Series Model to Forecast Rice Production

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 negligible error. The objective of present work is to design & implement such a competent fuzzy time series model for forecasting of rice production. We have proposed forecasting model based on fuzzy time series that highlights the impact of trend & seasonal components by yielding dynamic change of values from time t to t+1. The aim of using fuzzy time series is to deal with forecasting under the fuzzy environment that contains uncertainty, vagueness and imprecision. This method assigns importance to fuzzy intervals on the basis of frequency of number of time series data. Subsequently, computed fuzzy logical relations are used for analysis of time series rather than random and non-random functions as in case of usual time series analysis. Performance of the proposed model is demonstrated and compared with few preexisting forecasting methods on rice production. To prove robustness and accuracy of the presented model, analysis is performed on forecasting of enrollment data of university of Alabama.