Echo state network and classical statistical techniques for time series forecasting: A review

Forecasting is an extensive field of study, which tries to avoid injuries, diseases, and damages but also can help in energy production, finance investments, etc. Two mathematics modeling techniques have obtained promising results: the ones based on Machine Learning (Echo State Network) and based on Statistical techniques (ARIMA/GARCH). To take advantage of both techniques, we aimed to perform a systematic literature review of Echo State Network and classical Statistical techniques for forecasting Time Series. We conducted the searches on the databases ACM, IEEE Xplore, Scopus, and Web of Science and, after, did a bibliometric and a content qualitative analysis of the selected articles. We present the techniques and sources of the data set used, the most used keywords in the articles, analyze the reservoir computing/echo state network and statistical techniques, and comment on each article selected. From the analysis of this review, it is possible to infer that it is still an area to be studied more deeply and that the academy, even if timidly, never stopped using the echo state network for time series regression in general and financial series.