Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance of model selection, we propose a simple and reliable algorithm to improve the forecasting performance. Specifically, we construct multiple time series with different sub-seasons from the original time series. These derived series highlight different sub-seasonal patterns of the original series, making it possible for the forecasting methods to capture diverse patterns and components of the data. Subsequently, we produce forecasts for these multiple series separately with classical statistical models (ETS or ARIMA). Finally, the forecasts are combined. We evaluate our approach on widely-used forecasting competition data sets (M1, M3, and M4) in terms of both point forecasts and prediction intervals. We observe performance improvements compared with the benchmarks. Our approach is particularly suitable and robust for the data with higher frequency. To demonstrate the practical value of our proposition, we showcase the performance improvements from our approach on hourly load data that exhibit multiple seasonal patterns.

翻译：时间序列预测在现代商业决策中发挥着越来越重要的作用。在当今数据丰富的环境中,人们往往要选择数据的最佳预测模型。然而,确定最佳模型需要专业知识和经验,准确预测具有挑战性的任务。为了减轻模型选择的重要性,我们提议了一个简单可靠的算法来改善预测性能。具体地说,我们用最初时间序列中不同的分季节序列来构建多个时间序列。这些衍生序列突出原始系列的不同季节以下模式,使得预测方法能够捕捉数据的不同模式和组成部分。随后,我们将这些多系列的预测与传统的统计模型(ETS或ARIMA)分开进行。最后,这些预测是结合的。我们从点预报和预测间隔的角度评价我们广泛使用的竞争预测数据集(M1、M3和M4)的方法。我们观察与基准相比的绩效改进。我们的方法特别适合和有力,用于更频繁的数据。为了显示我们的观点的实际价值,我们用小时负荷数据的方法展示了绩效的改进情况,展示了多季节模式。