Abstract
A time series is a predetermination of data points that happen in repeated order of time. Forecasting productions play a necessary part in several fields such as, meteorological data, weather data, stock market data, rainfall data, agriculture data and so on. In recent years, fuzzy time series is used for forecasting. Song and Chissom (1993) proposed fuzzy time series for forecasting enrollments of data. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Neural networks for Radial Basis Function (RBF) and Multilayer Perceptran (MLP) and fuzzy time series for predicting wheat production of India were compared. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were compared. The results were displayed numerically and graphically.
Conflict of Interest
The authors declare no conflict of interest.
Ethical Approval
Not applicable
Data Availability
The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].
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