A DEEP LEARNING PARADIGM FOR AGRICULTURE MARKET PRICE FORECASTING

Abstract

ABSTRACT: Fluctuations in agriculture market prices are influenced by a multitude of factors, making accurate predictions challenging. LSTM networks, a type of recurrent neural network, have shown promise in capturing temporal dependencies in sequential data, making them suitable for time series prediction tasks like market prices. This project focuses on utilizing LSTM to enhance the precision of agricultural price forecasts. Agriculture market price prediction is a pivotal task with significant implications for both producers and consumers. This project introduces an innovative approach by harnessing the power of Long Short-Term Memory (LSTM) networks to tackle the complexities of predicting agricultural commodity prices.