Prior research in forecasting time series with Neural Networks (NN) has provided inconsistent evidence on their predictive accuracy. In management, NN have shown only inferior performance on well established benchmark time series of monthly, quarterly or annual frequency. In contrast, NN have shown preeminent accuracy in electrical load forecasting on daily or hourly time series, leading to successful real life applications. While this inconsistency has been traditionally attributed to the lack of a reliable methodology to model NNs, recent research indicates that the particular data properties of high frequency time series may be equally important. High frequency time series of daily, hourly or even shorter time intervals pose additional modelling challenges in the length and structure of the time series, which may abet the use of novel methods. This analysis aims to identify and contrast the challenges in modelling NN for low and high frequency data in order to develop a unifying forecasting methodology tailored to the properties of the dataset. We conduct a set of experiments in three different frequency domains of daily, weekly and monthly data of one empirical time series of cash machine withdrawals, using a consistent modelling procedure. While our analysis provides evidence that NN are suitable to predict high frequency data, it also identifies a set of challenges in modelling NN that arise from high frequency data, in particular in specifying the input vector, and that require specific modelling approaches applicable to both low and high frequency data.