Golestani Abbas, Gras Robin
School of Computer Science, University of Windsor, ON N9B 3P4, Canada.
1] School of Computer Science, University of Windsor, ON N9B 3P4, Canada [2] Department of Biology, University of Windsor, ON N9B 3P4, Canada [3] Great Lakes Institutes for Environmental Research, University of Windsor, ON N9B 3P4, Canada.
Sci Rep. 2014 Oct 30;4:6834. doi: 10.1038/srep06834.
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.
时间序列预测对于包括地震预测、金融市场预测和癫痫发作预测在内的各种领域都至关重要。我们提出了一种原创方法，为长期时间序列预测领域带来了全新视角。评估时间序列的非线性特性并将其用于长期预测。我们使用金融时间序列、医学时间序列和气候时间序列来评估我们的方法。我们获得的结果表明，复杂非线性时间序列的长期预测不再不切实际。新方法有能力预测股票市场时间序列的长期演变趋势，并且基于21名癫痫患者的数据，它在提前17分钟预测癫痫发作方面达到了100%的灵敏度和特异性的准确率水平。我们的新方法还以高度的准确性预测了过去30年全球气温上升的趋势。因此，我们的长期时间序列预测方法远优于现有方法。我们因此相信，我们提出的方法有潜力应用于许多其他领域，以生成准确且有用的长期预测。
