Can we predict the unpredictable? View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-10-30

AUTHORS

Abbas Golestani, Robin Gras

ABSTRACT

Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We used financial time series, medical time series and climate time series to evaluate our method. The results we obtained show that the long-term prediction of complex nonlinear time series is no longer unrealistic. The new method has the ability to predict the long-term evolutionary trend of stock market time series, and it attained an accuracy level with 100% sensitivity and specificity for the prediction of epileptic seizures up to 17 minutes in advance based on data from 21 epileptic patients. Our new method also predicted the trend of increasing global temperature in the last 30 years with a high level of accuracy. Thus, our method for making long-term time series predictions is vastly superior to existing methods. We therefore believe that our proposed method has the potential to be applied to many other domains to generate accurate and useful long-term predictions. More... »

PAGES

6834

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep06834

DOI

http://dx.doi.org/10.1038/srep06834

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003566494

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25355427


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