论文标题
FETSMC：基于功能的ETS模型组件选择
fETSmcs: Feature-based ETS model component selection
论文作者
论文摘要
发达的ET（指数平滑或误差，趋势，季节性）方法在状态空间表示中纳入了指数平滑模型家族，已广泛用于自动预测。现有的ETS方法使用信息标准来选择模型选择，通过在适用于给定时间序列的所有模型中选择一个具有最小信息标准的最佳模型。当应用于大规模时间序列数据时，这种模型选择方案下的ETS方法具有计算复杂性。为了解决此问题，我们通过模拟数据上的培训分类器提出了一种有效的ETS模型选择方法，以预测给定时间序列的适当模型组件形式。我们提供了一项模拟研究，以显示模拟数据中提出的方法的模型选择能力。我们根据点预测和预测间隔，对广泛使用的预测竞争数据集M4评估我们的方法。为了证明我们方法的实际价值，我们在每月的医院数据集上展示了方法的绩效改进。
The well-developed ETS (ExponenTial Smoothing or Error, Trend, Seasonality) method incorporating a family of exponential smoothing models in state space representation has been widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with the smallest information criterion among all models fitted to a given time series. The ETS method under such a model selection scheme suffers from computational complexity when applied to large-scale time series data. To tackle this issue, we propose an efficient approach for ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We provide a simulation study to show the model selection ability of the proposed approach on simulated data. We evaluate our approach on the widely used forecasting competition data set M4, in terms of both point forecasts and prediction intervals. To demonstrate the practical value of our method, we showcase the performance improvements from our approach on a monthly hospital data set.
