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In this work the self-organizing fuzzy neural network (SOFNN) is employed to create an accurate and easily calibrated approach to multiple-step-ahead prediction for the NN5 forecasting competition 2008. The competition dataset consists of 111 daily empirical time series of cash-machine withdrawals. The objective for the competition was to forecast future transactions up to 56 days ahead with the highest prediction accuracy using a single methodology. The SOFNN is a highly efficient and accurate algorithm for time series-prediction which learns from data incrementally and can autonomously adapt its structure in the learning process to cope with drifts in the data dynamics. It can also modify its architecture autonomously to suit different prediction horizons, embedding dimensions and time lags. Standard neural networks(NNs) and autoregressive(AR) models are employed as benchmarks for comparison. It is shown through a statistical analysis of the results, that the SOFNN significantly outperforms the NN and AR methods.