N. Kourentzes and S. F. Crone, 2008, Proceedings of the 2nd European Symposium on Time Series Prediction, ESTSP’08, Helsinki, Finland.
In time series prediction, modelling neural networks poses multiple challenges in specifying suitable input vectors, network architectures, and training parameters depending on the underlying structure of the time series data. The data properties are often determined by the frequency in which the time series is measured, such as low frequency data of yearly, quarterly or monthly observations, or high frequency data of weekly, daily, hourly or even shorter time intervals. As different time frequencies require distinct modelling heuristics, employing neural networks to predict a set of time series of unknown domain, which may exhibit different characteristics and time frequencies, remains particularly challenging and limits the development of fully automated forecasting methodologies for neural networks. We propose a methodology that unifies proven statistical modelling approaches based upon filters and best practices from previous forecasting competitions into one framework, providing automatic forecasting without manual intervention by inferring all information from the data itself to model a diverse set of time series of varying time frequency, like the ESTSP’08 dataset.