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.
翻译:完善的ETS方法(扩大滑动或误差、趋势、季节性)结合国家空间代表系统中的指数滑动模型组合,已被广泛用于自动预测。现有的ETS方法在选择模型时采用信息标准,即选择一个最佳模型,在适合特定时间序列的所有模型中采用最小信息标准。这种模式选择方法在应用大型时间序列数据时具有计算复杂性。为了解决这一问题,我们提议了一种有效的方法,通过培训分类人员模拟数据来选择ETS模型,以预测特定时间序列中的适当模型组件。我们提供模拟研究,以显示模拟数据拟议方法的模型选择能力。我们从点预报和预测间隔的角度评估了我们广泛使用的竞争预测数据集M4的方法。为了展示我们方法的实际价值,我们用医院月度数据集展示了我们方法的绩效改进。