The recurrent neural networks are a very appropriate tool for modeling time series. This is a type of network architecture that implements some kind of memory and, therefore, a sense of time. This is achieved by implementing some neurons receiving as input the output of one of the hidden layers, and injecting their output again in that layer. In this article I will show a simple way to use two neural networks of this kind, the Elman and Jordan ones, using the program R.
In some other articles in this blog, I have already written about complex time series, recurrence quantification analysis (RQA) and neural networks. In this series of articles, I will discuss some points to take into account when combining the use of these two tools to identify patterns in complex series, such as detecting anomalies in electrocardiograms or electroencephalograms.