Strategies for time series forecasting with generalized regression neural networks

This paper discusses how to forecast time series using generalized regression neural networks. The main goal is to take advantage of their inherent properties to generate fast, highly accurate forecasts. To this end, the key modeling decisions involved in forecasting with generalized regression neural networks are described. To deal with every modeling decision, several strategies are proposed. Each strategy is analyzed in terms of forecast accuracy and computational time. Apart from the modeling decisions, any successful time series forecasting methodology has to be able to capture the seasonal and trend patterns found in a time series. In this regard, some clever techniques to cope with these patterns are also suggested. The proposed methodology is able to forecast time series in an automatic way. Additionally, the paper introduces a publicly available R package that incorporates the best presented modeling approaches and transformations to forecast time series with generalized regression neural networks. (C) 2021 The Author(s). Published by Elsevier B.V.

Automated summary

This paper discusses the use of generalized regression neural networks for time series forecasting, aiming at fast and accurate forecasts. The key modeling decisions and proposed strategies are analyzed in terms of forecast accuracy and computational time. Additionally, clever techniques for capturing seasonal and trend patterns in time series are suggested. The paper also introduces a publicly available R package that incorporates the best modeling approaches and transformations for making forecasts with generalized regression neural networks.