Sales forecast in an IT company using time series

The sales forecast is fundamental for the planning of the activity of the companies providing, important indicators for the support of the decisions of the managers. This study aims to explore the potential of time series prediction algorithms in an IT company. The forecast was based on the company's billing data for 192 months of activity. The analysis of the data was based on the Cross Industry Standard Process for Data Mining approach and for the treatment; we used the Anaconda IPython and Pandas. We developed the prediction with three models using R: Exponential Smoothing (Holt-Winters), autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN). The comparison of the performance of each of the methods shows that the model based on artificial neural networks has a greater accuracy in the prediction. These results need deepening the study to broaden the universe of the studied contexts. However, the simplicity in the application of the artificial neural networks model makes possible its use in computer applications without specific knowledge, giving a reliable instrument that allows the supporting decision-making by managers.