Prediction of leather footwear export using learning algorithms based on ANN model

Abstract

Leather footwear export plays a crucial role in the Indian economy as India is the second largest footwear producer in the world. As a commodity, it is unavoidable to emphasize its export performance by forecasting. This paper aims to bring out an Artificial Neural Network based model to predict India’s leather footwear export. Towards forecasting Leather footwear export, the dataset comprising five commodities covered under leather footwear has been taken from 1996 to 97 to 2021–22. The authors have proposed India’s Leather Footwear Export - Artificial Neural Network (ILFE-ANN) model with SGD optimizer and activation functions such as Sigmoid / Logistic and Rectified linear unit (ReLU). The authors have kept null values as it is in the data than replacing them with imputation methods such as mean and median while modelling. Outliers are replaced with the mean value of the remaining data before modelling. Moreover, different learning algorithms such as Adaptive Moment (Adam), RMS Propagation (RMSProp), Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) have been compared to choose an optimal one before being implemented in the ILFE-ANN. The validation of the ILFE-ANN model has been implemented for the prediction of livestock population and compared with the Regression model. The variation percentage confirms that the proposed ILFE-ANN model performs significantly with 0.51% RMSProp, 1.68 % SGDM and 2.54 % SGD. Further, the minimum value of performance metrics MAE, MAPE and RMSE obtained are 0.4, 0.5 and 0.5 respectively for the prediction of sheep population for the year 2017. It shows that SGD performs better with the least error rate of 8% MAPE for the export of leather Commodity 64035113. Hence, the study confirms that the ANN model with SGD optimizer performs better for the prediction of India’s leather footwear trade data.