Predicting time series using a neural network as a method of distinguishing chaos from noise

A neural-network approach is presented for making short-term predictions on time series. The neural network does better at short-term predictions of a chaotic signal than does an optimum autoregressive model. Also, the neural network is clearly capable of distinguishing between chaos and additive noise.

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