论文标题
半自动化的同时预测器选择回归sarima模型
Semi-automated simultaneous predictor selection for Regression-SARIMA models
论文作者
论文摘要
确定使用哪些预测因子在在广泛应用中得出统计模型中起着不可或缺的作用。在预测电信网络中事件的挑战中，我们为线性回归模型提出了一个半自动，联合模型拟合和预测指标选择程序。我们的方法可以对回归残差中的串行相关性进行建模和说明，从而产生稀疏且可解释的模型，并可用于共同选择一组相关响应的模型。这是通过使用最近开发的混合整数二次优化方法的概括对非零系数数量的拟合线性模型来实现的。与行业当前使用的方法相比，我们方法的最终模型在激励电信数据上实现了更好的预测性能。
Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a telecommunications network, we propose a semi-automated, joint model-fitting and predictor selection procedure for linear regression models. Our approach can model and account for serial correlation in the regression residuals, produces sparse and interpretable models and can be used to jointly select models for a group of related responses. This is achieved through fitting linear models under constraints on the number of non-zero coefficients using a generalisation of a recently developed Mixed Integer Quadratic Optimisation approach. The resultant models from our approach achieve better predictive performance on the motivating telecommunications data than methods currently used by industry.
