Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series

Abstract

Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.