Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-world situations, the data distributions are not stationary and they can change over time while reducing the accuracy of the forecasting models, which in the ML literature is known as concept drift. Handling concept drift in forecasting is essential for many ML methods in use nowadays, however, the prior work only proposes methods to handle concept drift in the classification domain. To fill this gap, we explore concept drift handling methods in particular for Global Forecasting Models (GFM) which recently have gained popularity in the forecasting domain. We propose two new concept drift handling methods, namely: Error Contribution Weighting (ECW) and Gradient Descent Weighting (GDW), based on a continuous adaptive weighting concept. These methods use two forecasting models which are separately trained with the most recent series and all series, and finally, the weighted average of the forecasts provided by the two models are considered as the final forecasts. Using LightGBM as the underlying base learner, in our evaluation on three simulated datasets, the proposed models achieve significantly higher accuracy than a set of statistical benchmarks and LightGBM baselines across four evaluation metrics.

翻译：基于机器学习的时间序列预测模型在生成预测时，通常要求并假设数据具有某种程度的平稳性。然而，在许多实际场景中，数据分布并非平稳，它们会随时间变化，从而降低预测模型的精度——这在机器学习文献中被称为概念漂移。在预测中处理概念漂移对于当今使用的许多机器学习方法至关重要，然而现有工作仅提出了处理分类领域概念漂移的方法。为填补这一空白，我们专门针对近年来在预测领域备受关注的全局预测模型（GFM），探索了概念漂移处理方法。我们提出两种新的概念漂移处理方法，即：基于连续自适应加权概念的误差贡献加权（ECW）和梯度下降加权（GDW）。这两种方法分别使用两个预测模型，分别用最新序列和全部序列进行训练，最终将这两个模型提供的预测的加权平均值作为最终预测。以LightGBM作为底层基学习器，我们在三个模拟数据集上的评估表明，所提模型在四个评估指标上均显著优于一组统计基准模型和LightGBM基线模型。