Basic Food Basket Monthly Price of Southern Bahia Cities: A Time Series Forecasting with Deep Learning Using a Recurrent Neural Network Approach

Abstract

The goal of time series analysis is extract non-trivial information from chronological sorted points. Time series data creation it is not a difficult task, however the same cannot be said about its analysis and predictions, with these processes being considered in 2006 one of the ten main challenges of data mining research field. The time series utilized in this research was an economic time series with the monthly prices of the basic food basket in Ilheus and Itabuna cities. When analyzing time series, different methods can be applied, but recently the methods related to Deep Learning are becoming more popular, with recurrent neural networks (RNNs) being the most popular among them. This paper experimented an RNN architecture in three different experiments, predicting in different time terms the prices of the basic food basket time series of 2019 and 2020. All experiments presented a satisfactory prediction result and sensitivity to the real values series behavior. These results suggest that the RNN architecture can generalize well the time series to periods yet not seen by the network and that the NN can have an even better performance in a more data abundant environment.