Abstract This discussion extends published findings on the use of artificial intelligence models for time series modeling/monthly streamflow forecasting at the Port Elgin station on the Saugeen River, Canada. Published results are applied and run in autoregressive (AR) and moving average (MA) models as well as hybrids of these with autoregressive conditional heteroscedasticity (ARCH), namely AR-ARCH and MA-ARCH. The hybrid solutions are concluded to be superior to both linear and nonlinear modeling approaches. However, common nonlinear methods including neural networks have a recognized defect in time series forecasting known as “inappropriate time series modeling inputs”. The present study addresses this significant source of error in nonlinear modeling by referring to time series components via suitable time series preprocessing. Of particular interest in this discussion provides the novel vision for time series modeling using nonlinear approaches. The nature of the hydrological variables in time series modeling has great impact on the predicted output and should thus be considered in the modeling procedure. An appropriate preprocessing technique must also be considered carefully in order to attain reliable nonlinear modeling results.

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由 Saeid Mehdizadeh、Farshad Fathian、Mir Jafar Sadegh Safari 和 Jan F. Adamowski 讨论“时间序列和人工智能模型的比较评估以估计月流量：本地和外部数据分析方法”

摘要 本讨论扩展了已发表的关于在加拿大索金河的埃尔金港站使用人工智能模型进行时间序列建模/月流量预测的发现。已发布的结果在自回归 (AR) 和移动平均 (MA) 模型以及这些模型与自回归条件异方差性 (ARCH) 的混合模型中应用和运行，即 AR-ARCH 和 MA-ARCH。混合解决方案的结论是优于线性和非线性建模方法。然而，包括神经网络在内的常见非线性方法在时间序列预测中存在一个公认的缺陷，称为“不适当的时间序列建模输入”。本研究通过适当的时间序列预处理参考时间序列分量来解决非线性建模中这一重要的错误来源。在此讨论中特别感兴趣的是使用非线性方法为时间序列建模提供了新的愿景。时间序列建模中水文变量的性质对预测输出有很大影响，因此应在建模过程中加以考虑。为了获得可靠的非线性建模结果，还必须仔细考虑适当的预处理技术。