A Comparative Study of VAR and ANN Models for the Forecasting of Eggplant Wholesale Prices in Lucknow, India

Abstract

Accurate forecasting of agricultural commodity prices, particularly vegetables, is essential for comprehending market dynamics and maintaining economic stability among stakeholders. Effective price forecasting fosters agricultural sustainability and economic resilience. This study aims to identify the optimal model for forecasting wholesale eggplant prices in the Lucknow market, comparing the traditional vector autoregressive (VAR) model with the Artificial Neural Network (ANN) model. The most commonly used evaluation metrics were applied to compare and assess model performance. For model building, 204 monthly observations from January 2008 to December 2024, comprising a dataset of wholesale prices and total arrival of eggplant in the Lucknow center, were utilized. In the modeling process, the entire dataset was partitioned into two subsets, training and testing data, with a split of 80:20. To check stationarity, the Augmented Dickey-Fuller test was conducted, and the lag order for the VAR model was selected based on the minimum AIC value, with [5] as the optimal lag order. Similarly, the ANN model was created using [5] lags of the variables, as independent variables. The findings indicate that the ANN [10: 3: 2: 1] model surpasses the VAR (5) model in forecasting wholesale prices within the test dataset. However, both models exhibit overfitting, likely attributable to the VAR model's inadequacy in capturing the heteroscedasticity effect and the ANN model's dependence on a limited dataset and the number of lag variables incorporated as inputs. These methods can be applied to model and forecast prices of other agricultural commodities and have potential in broader agricultural research, as their utilization in this field remains limited.