Forecasting has attracted a lot of research interest, with very successful methods for periodic time sequences. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic as well as chaotic time sequences. We use the technique of delay coordinate embedding, which needs several parameters; our contribution is the automated way of setting these parameters, using the concept of "intrinsic dimensionality." Our operational system has fast and scalable algorithms for preprocessing and, using R-trees, also has fast methods for forecasting. The result of this work is a black-box which, given a time series as input, fi nds the best parameter settings, and generates a prediction system. Tests on real and synthetic data show that our system achieves low error, while it can handle arbitrarily large datasets. 36 pages

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