论文标题
基于时间序列预测的随机NN的增强合奏学习
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting
论文作者
论文摘要
时间序列预测是一个具有挑战性的问题，尤其是当时间序列表达多种季节性，非线性趋势和变化的差异时。在这项工作中，为了预测复杂的时间序列，我们提出了基于随机神经网络的集合学习，并以三种方式提高。这些包括基于残差，校正目标和相反反应的合奏学习。使用后两种方法来确保所有合奏成员都解决了类似的预测任务，这证明在结合的所有阶段都使用了完全相同的基本模型。所有成员的任务统一都简化了整体学习，并导致预测准确性提高。在一项实验研究中证实了这一点，该研究涉及预测时间序列具有三重季节性，在其中我们比较了三种合奏增强的变体。基于RANDNNS的拟议合奏的强调是非常快速的训练和基于模式的时间序列表示，从时间序列提取相关信息。
Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise ensemble learning based on residuals, corrected targets and opposed response. The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling. Unification of the tasks for all members simplifies ensemble learning and leads to increased forecasting accuracy. This was confirmed in an experimental study involving forecasting time series with triple seasonality, in which we compare our three variants of ensemble boosting. The strong points of the proposed ensembles based on RandNNs are extremely rapid training and pattern-based time series representation, which extracts relevant information from time series.
