Demand and Price Forecasting Using Deep Learning Algorithms

Abstract

Marketing agricultural products is always a challenge. The farmers are forced to sell their products at a very low price to the middlemen. Meanwhile, consumers are deprived of fresh farm products. Rythu Bazaar, the farmers’ market, was established by the Andhra Pradesh government in the year 1999 to address this issue. Consumers can purchase fresh items directly from farmers, and farmers can sell their goods at the desired price. As a result, the present study examines various parameters where consumers and farmers have benefited from a varity of products such as vegetables, fruits, and so on. In this paper, we try to forecast the product price, which is going to help farmers as well as consumers. We are going to use some statistical approaches to prove that consumers are getting benefited from the Rythu Bazar. In our research, we want to forecast for a long time, i.e., 1 year. We are going to use normal machine learning algorithms along with a few Deep Learning algorithms like RNN (Recurrent neural networks), LSTM (Long short-term memory), and Bidirectional LSTM. We intend to investigate many techniques in this field, evaluate the existing algorithms, examine several Deep Learning models, and frame the best model enhancements that produce the greatest demand forecasting outcomes during our research.