Forecast of prices for horticultural products with the use of artificial neural networks

Artificial neural networks (ANN) are becoming increasingly popular, acting as a very important tool to aid in the interpretation of the market. They have been used with benefits in time series analysis, as they provide an easy mathematical treatment and faster results, facilitating decision-making. Currently in the field of business, many systems using neural networks have worked well in identifying complex patterns, learning by experience, reaching conclusions and making predictions. This article deals with the application of ANN for predicting vegetable prices due to seasonality. The networks were trained using time series data for vegetables prices from the database of the Nucleo de Estudos e Pesquisas Economicas e Sociais (NEPES) {Center for Studies and Economic and Social Research} at Universidade Anhanguera Uniderp of Campo Grande (MS), Brazil. The results were very promising and encouraging because it was possible to forecast prices of these foods over time, serving as a good tool to help entrepreneurs in the horticultural industry. This method is very useful because it can be applied also in the retail trade and industry in helping entrepreneurs in these sectors in decision-making.   Key words: Vegetable, time series forecasting, artificial neural networks (ANN) training, artificial neuron, horticultural industry.