Thesis (MSc (Computer Science))-- University of Stellenbosch, 2001. / ENGLISH ABSTRACT: This thesis explores the use of neural networks for predicting difficult, real-world time series. We first establish and demonstrate methods for characterising, modelling and predicting well-known systems. The real-world system we explore is seismic event data obtained from a South African gold mine. We show that this data is chaotic. After preprocessing the raw data, we show that neural networks are able to predict seismic activity reasonably well. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van neurale netwerke om komplekse, werklik bestaande tydreekse te voorspel. Ter aanvang noem en demonstreer ons metodes vir die karakterisering, modelering en voorspelling van bekende stelsels. Ons gaan dan voort en ondersoek seismiese gebeurlikheidsdata afkomstig van ’n Suid-Afrikaanse goudmyn. Ons wys dat die data chaoties van aard is. Nadat ons die rou data verwerk, wys ons dat neurale netwerke die tydreekse redelik goed kan voorspel. / Integrated Seismic Systems International