Artificial neural networks are powerful tools for time series forecasting. The problem addressed in this article is to do multi-step prediction of a stationary time series, and to find the associated prediction limits. Artificial neural network models for time series are non-linear. However, results that are applicable to linear models are sometimes mistakenly applied to non-linear models. One example where this is observed is in multi-step forecasting. A bootstrap method is proposed to calculate one- and multi-step predictions and prediction limits. The results are applied to an electricity load time series as well as to a pure autoregressive time series.
Kunsmatige neurale netwerke is kragtige instrumente vir tydreeksvoorspelling. In hierdie artikel word multistap-vooruitberaming van ‘n stasionêre tydreeks en die gepaardgaande vertroueinterval behandel. Resultate wat slegs geldig is vir lineêre modelle word soms verkeerdelik op neurale netwerkmodelle toegepas. ‘n Voorbeeld hiervan kom in multistap-voorspelling voor. ‘n Skoenlusmetode, word voorgestel waarvolgens eenstap- en multistap- voorspellings en vertroueintervalle bereken kan word. Die resultate word op ‘n elektrisiteitslastydreeks en op ‘n suiwer outoregressiewe tydreeks toegepas.
