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Immunocomputing for Spatio-Temporal Forecast
Abstract
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal processing. The approach includes both low-level feature extraction and highlevel (“intelligent”) pattern recognition. The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization both controlled by cytokines (messenger proteins). Such FIN can be formed from raw signal using discrete tree transform (DTT), singular value decomposition (SVD), and the proposed index of inseparability in comparison with the Renyi entropy. Real-world application is demonstrated on data of space monitoring of the Caspian, Black, and Barents Sea. A surprising result is strong negative correlation between anomalies of sea surface temperature (SST) and sunspot number (SSN). This effect can be utilized for long-term STF.
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