A Connectionist System for Medium-term Horizon Time Series Prediction
نویسنده
چکیده
In this paper, we propose some improvements for the problem of time series prediction with neural networks where a medium-term prediction horizon is needed. In particular, the ionospheric prediction service of the french Centre National d' Etudes des T el ecommunica-tions needs a six-month ahead prediction of a sunspots related time series which has a strong innuence on wave propagation in ionosphere. The proposed improvements consist in two diierent modular architec-tures and a way to increase the size of the training set. Experimental results are compared to those of a simple multi-layer perceptron.
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