Forecasting time series combining machine learning and Box-Jenkins time series

In statistics, Box-Jenkins Time Series is a linear method widely used to forecasting. The linearity makes the method inadequate to forecast real time series, which could present irregular behavior. On the other hand, in artificial intelligence FeedForward Artificial Neural Networks and Continuous Machine Learning Systems are robust handlers of data in the sense that they are able to reproduce nonlinear relationships. Their main disadvantage is the selection of adequate inputs or attributes better related with the output or category. In this paper, we present a methodology that employs Box-Jenkins Time Series as feature selector to Feedforward Artificial Neural Networks inputs and Continuous Machine Learning Systems attributes. We also apply this methodology to forecast some real time series collected in a power plant. It is shown that Feedforward Artificial Neural Networks performs better than Continuous Machine Learning Systems, which in turn performs better than Box-Jenkins Time Series.