Strategies for time series forecasting with generalized regression neural networks

Francisco Martínez · Francisco Charte · María Pilar Frías · Ana María Martínez-Rodríguez

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Forecasting Techniques and Applications Neural Networks and Applications Time Series Analysis and Forecasting

Abstract

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.

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References

1992Testing the null hypothesis of stationarity against the alternative of a unit root 2006Another look at measures of forecast accuracy 2008Automatic Time Series Forecasting: The forecast Package for R 2009Time series analysis forecasting and control 2003Financial time series forecasting using support vector machines 2000The M3-Competition: results, conclusions and implications 2000Out-of-sample tests of forecasting accuracy: an analysis and review 2019The M4 Competition: 100,000 time series and 61 forecasting methods

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