Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time series forecasting methods, however, have resulted in small models with limited expressive power because they have difficulty in scaling their model size up while maintaining high accuracy. In this paper, we propose Forecasting orchestra (Forchestra), a simple but powerful framework capable of accurately predicting future demand for a diverse range of items. We empirically demonstrate that the model size is scalable to up to 0.8 billion parameters. The proposed method not only outperforms existing forecasting models with a significant margin, but it could generalize well to unseen data points when evaluated in a zero-shot fashion on downstream datasets. Last but not least, we present extensive qualitative and quantitative studies to analyze how the proposed model outperforms baseline models and differs from conventional approaches. The original paper was presented as a full paper at ICDM 2022 and is available at: https://ieeexplore.ieee.org/document/10027662.

翻译：时间序列预测是许多商业问题（包括需求预测和物流优化）中最基本且普遍存在的任务之一。然而，传统的时间序列预测方法产生的模型规模较小且表达能力有限，因为其在保持高精度的同时难以扩展模型规模。本文提出Forecasting Orchestra（Forchestra）——一个简洁但强大的框架，能够准确预测多种商品的未来需求。我们通过实验证明，该模型规模可扩展至高达8亿参数。所提方法不仅以显著优势超越现有预测模型，还能在零样本方式下对下游数据集进行评估时，良好地泛化至未见数据点。最后，我们通过大量定性与定量研究，分析了所提模型如何优于基线模型，以及其与传统方法的区别。原论文作为完整论文发表于ICDM 2022，可查阅：https://ieeexplore.ieee.org/document/10027662。