H. Boraine, V.S.S. Yadavalli


ENGLISH ABSTRACT: 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.

AFRIKAANSE OPSOMMING: Kunsmatige neurale netwerke is kragtige instrumente vir tydreeksvoorspelling. In hierdie artikel word multistap-vooruitberaming van n stasione tydreeks en die gepaardgaande vertroueinterval behandel. Resultate wat slegs geldig is vir linee 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.

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DOI: https://doi.org/10.7166/14-2-263


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