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Wheat Production Prediction in India using ARIMA, Neural Network and Fuzzy Time Series

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.

Keywords

ARIMA, Fuzzy Time Series, MAE, MAPE, neural network, Residual Analysis and Prediction, RMSE

  • License

    Creative Commons Attribution 4.0 (CC BY 4.0)

  • Language & Pages

    English, NA

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