FORECASTING VEGETABLE PRICE USING TIME SERIES DATA

Abstract

Predicting the vegetable price is essential in agriculture sector for effective decision making. This forecasting task is quite difficult. Neural network is self-adapt and has excellent learning capability and used to solve variety of tasks that are intricate. This model is used to predict the next day price of vegetable using the previous price of time series data. The three machine learning algorithms are incorporated in this work namely Radial basis function, back propagation neural network and genetic based neural network are compared. The models are assessed and it is concluded from the derived accuracy that the performance of genetic based neural network is better than back propagation neural network and radial basis function and improves the accuracy percentage of vegetable price prediction.