An Improved Arima Model Using Ann and Svm for Forecasting Rapeseed & Mustard Production - Borsha Neog (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Forecasting of agricultural crop production is the art of predicting production before harvest and is crucial for planning and policy making at various stages. “Rapeseed & Mustard (R & M) is the predominant oilseed crop of Assam because of its short duration. To know about futures estimates, planning is much importance to make fruitful decisions. In this context, the present study was undertaken to develop proper forecasting models for R & M of Assam. Here we have been used yearly data on production of Rapeseed & Mustard for forecasting from the year 1951 to 2018. For model building, we have used data from 1951-1998 and for model testing data from 1999 - 2018 were used for forecasting performance of the model. In this study, to analyse the past behaviour of the production of Rapeseed & Mustard to make interpretations about its future behaviour using different models Autoregressive integrated moving average (ARIMA), Artificial neural network (ANN), support vector machine (SVM) and hybrid of both ARIMA-ANN, ARIMA-SVM. For the selected crops, ARIMA (0,1,0) model was selected as a suitable model. In training, mean absolute error (MAE) for hybrid ARIMA (0,1,0)-SVM was found to be 8216.169 as compare to 8813.731 of ARIMA-ANN; 10620.825 of ARIMA (0,1,0); 10242.319 of ANN; 9831.046 of SVM. In testing, MAE for hybrid ARIMA (0,1,0)-SVM was found to be 8174.671 as compare to as compare to 9263.464 of ARIMA-ANN; 10606.565 of ARIMA (0,1,0); 10384.249 of ANN; 10139.604 of SVM. Henceforth, the performances of hybrid ARIMA-ANN and ARIMA-SVM were found to be better than that of ARIMA for both under training as well as testing data sets. So from the results we can recommend hybrid approach gives better results for forecasting of Rapeseed & Mustard production.