S. F. Crone and N. Kourentzes, 2008, Proceedings of the International Conference on Data Mining, DMIN’08, Las Vegas, USA, CSREA, pp.37-42.
Prior research in forecasting economic time series with artificial Neural Networks (NN) has provided only inconsistent evidence on their predictive accuracy. In management science, NN were routinely evaluated on a set of well established benchmark time series of monthly, quarterly or annual frequency. In contrast, NN are accepted as a potent method for electrical load using time series that essentially display similar archetypical patterns of seasonality, trends, level shifts, outliers and calendar effects, but are of higher complexity and frequency. While this discrepancy has been attributed to the lack of a reliable methodology to determine the model parameters, recent research originating from econometrics and finance has indicated that high frequency data may pose different modelling problems compared to their low frequency counterpart and may hence benefit the use of dissimilar methods. This analysis aims to identify and contrast the challenges in modelling NN for low and high frequency data in order to develop a methodology tailored to the properties of the dataset. We conduct a set of experiments in three different frequency domains of daily, weekly and monthly empirical data of the same time series of cash machine withdrawals, using a consistent modelling procedure. The comparison against the naive model and exponential smoothing family models provides evidence that NN are suitable to predict high frequency data. Our analysis identifies a set of problems in modelling NN that arise in high frequency domain, mainly in specifying the input vector. To address these problems a different modelling approach is required between the low and high frequency data. Identifying these problems provides a starting point for the development of a unified methodology to forecast high frequency data with NN and facilitate revisions of the NN modelling approaches employed for low frequency data in management science.