RESEARCH INITIATION AWARD: New Methods in Neural Network Prediction: Using Fossil Records to Improve Solar Activity Forecasting

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9410823 Wan In recent years, neural networks have become increasingly popular for use in time series prediction. The principle investigator has developed specialized neural network architectures that have shown improvements over traditional neural predictors. this grant will allow continued development of these architectures while studying the extremely important times series discussed below. One of the most famous time series corresponds to the record of sunspots dating back to the 1700's. New insight in to this series come s form fossilized rocks formed during the Precambrian period in South Australia. Remarkably, striation widths in the fossils are believed to constitute over a thousand year record of solar activity. The shape and spectral characteristics are near identical to the support series. though obviously noisy, the data is rich in information. Using neural networks, the PI will research how this series may be used to improve our current predictions of sunspots. In general, it is an important problem of how related series at different times may be used to improve the prediction of one of them. From a practical perspective, the use for sunspot predictions are found in applications ranging form planning orbital space missions to safeguarding power grids.