Oilseeds production and yield forecasting using ARIMA-ANN modelling

Abstract

Agriculture, the backbone of Indian economy, lot of time series data on various parameters and these time series data generated from agriculture can be effectively modelled using various time-series modelling techniques such as Box-Jenkins ARIMA modelling technique, State-Space modelling technique, Structural Time Series modelling methodology and various other time series modelling methodologies depending upon the availability and properties of the given datasets. Modelling and forecasting of time-series data on all-India production and productivity of nine oilseed crops from 1950-51 to 2015-16 is carried out in this study. Modelling and subsequent forecasting for the datasets under consideration is performed using Autoregressive Moving Average (ARIMA), Artificial Neural Network (ANN) and ARIMA-ANN hybrid modelling methodologies. It is important to note that ARIMA is a linear modelling methodology whereas ANN is a non-linear modelling methodology. ARIMA-ANN is a hybrid of these two methodologies which can efficiently capture both linear and non-linear structures present in the dataset under consideration. For efficient performance of ARIMA modelling, the data under consideration needs to satisfy stationarity criterion. i.e. mean and variance should constant over a period of time. First, the dataset on oilseeds production and productivity are tested for stationarity and subsequent to non-stationarity of the original data, first order differenced series are considered for modeling using the Box-Jenkins approach. Various ARIMA models are developed and among them ARIMA (1,1,0) and ARIMA (1,1,1) are found suitable for the production and productivity data, respectively, based on the Information Criteria such as Akaike Information Criterion (AIC) and Schwarz-Bayesian Criterion (SBC). Among the developed ANN models, the Neural Network Autoregression (NNAR) of order NNAR (2,2) is found to be suitable for both the variables under study. Later, models using ANN-ARIMA hybrid methodology are developed and the ARIMA (1,1,0)-NNAR(1,1) and ARIMA (1,1,1)-NNAR (1,1) for production and productivity are found to be suitable, respectively. All the three models are tested for their forecast accuracy using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Accordingly, the ARIMA-ANN hybrid methodology is found to be superior to the individual ARIMA and ANN methodologies. Based on the efficient developed model viz., hybrid ARIMA-ANN model, forecasting annual all-India oilseeds production for the year 2022 is carried out and is found to be 35.6 (±3.2) million tonnes with a productivity of 1178 (±94.6) kg/hectare.