ANN and ARIMA article summary

I would like to draw your attention to a paper published on the ASOR Bulletin in 2003. It's titled "comparing ANN Based Models with ARIMA for Prediction of Forex Rates". It's an interesting paper written in plain language. Let me give you a summary of the main ideas:
- the ANN models used (including SB, SCG and BR)all outperformed the ARIMA models (Box-Jenkins);
- the analysis was about the forex market, six currency pairs in total;
- "ANN proved to be very effective in describing the dynamics of non-stationary time series due to its unique non-parametric, non-assumable, noise-tolerant, and adaptive approximators that can map any nonlinear function without a priori assumptions about the data";
- details about ARIMA are available in Jarret "Business Forecasting Methods";
- the idea of NN is explained;
- multilayer feedforward network (MFN) is described briefly;
- no study has been reported to analytically determine the generalization performance of each algorithm;
- the author uses MA5, MA10, MA20, MA60, MA120 (Moving averages of given lengths);
- the NN used had 6 inputs, 1 layer and one output;
- the statistical metrics used to evaluate the performance are: Mean Square Error, Mean Absolute Error, Directional Symmetry;
- neural networks models produce much better performance than the conventional ARIMA models for both shorter and longer term forecasting;