Automatic Time Series Forecasting with GRNN: A Comparison with Other Models

TitleAutomatic Time Series Forecasting with GRNN: A Comparison with Other Models
Publication TypeConference Paper
Year of Publication2019
AuthorsMartínez, Francisco, Charte Francisco, Rivera-Rivas A.J., and Frías María Pilar
Conference NameInternational Work-Conference on Artificial Neural Networks
Date Published05/2019
KeywordsAutomatic forecasting, General regression neural networks, time series forecasting

In this paper a methodology based on general regression neural networks for forecasting time series in an automatic way is presented. The methodology is aimed at achieving an efficient and fast tool so that a large amount of time series can be automatically predicted. In this sense, general regression neural networks present some interesting features, they have a fast single-pass learning and produce deterministic results. The methodology has been implemented in the R environment. A study of packages in R for automatic time series forecasting, including well-known statistical and computational intelligence models such as exponential smoothing, ARIMA or multilayer perceptron, is also done, together with an experimentation on running time and forecast accuracy based on data from the NN3 forecasting competition.