3.1 Autoregressive models

Autocorrelated data can be addressed by fitting models to the data and analyzing the residuals, instead of the variables. With ARIMA models, crosscorrelation between the variables is not accounted for, and although multivariate models can also be employed using this approach, it becomes a complex task when there are many variables (m > 10), owing to the high number of parameters that must be estimated, as well as the presence of crosscorrelation [3, 59].

Apart from ARIMA models, other models, such as neural networks [60–62], decision trees [63], and just-in-time-learning with PCA [64], have also been proposed.
