Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time-series forecasting and developing pre-trained universal models, a comprehensive examination of their capability in accommodating varied-horizon forecasting during inference is still lacking. This paper bridges this gap through the design and evaluation of the Elastic Time-Series Transformer (ElasTST). The ElasTST model incorporates a non-autoregressive design with placeholders and structured self-attention masks, warranting future outputs that are invariant to adjustments in inference horizons. A tunable version of rotary position embedding is also integrated into ElasTST to capture time-series-specific periods and enhance adaptability to different horizons. Additionally, ElasTST employs a multi-scale patch design, effectively integrating both fine-grained and coarse-grained information. During the training phase, ElasTST uses a horizon reweighting strategy that approximates the effect of random sampling across multiple horizons with a single fixed horizon setting. Through comprehensive experiments and comparisons with state-of-the-art time-series architectures and contemporary foundation models, we demonstrate the efficacy of ElasTST's unique design elements. Our findings position ElasTST as a robust solution for the practical necessity of varied-horizon forecasting.

翻译：众多工业领域需要能够提供跨不同时间跨度的鲁棒预测模型。尽管近年来在构建特定时序预测架构和开发预训练通用模型方面取得了显著进展，但对其在推理阶段适应多时间跨度预测能力的全面评估仍然缺乏。本文通过设计与评估弹性时序Transformer（ElasTST）来填补这一空白。ElasTST模型采用包含占位符和结构化自注意力掩码的非自回归设计，确保未来输出对推理时间跨度的调整保持不变。模型还集成了可调谐的旋转位置编码变体，以捕捉时序特定周期并增强对不同时间跨度的适应性。此外，ElasTST采用多尺度分块设计，有效整合细粒度和粗粒度信息。在训练阶段，ElasTST使用时间跨度重加权策略，通过单一固定跨度设置近似实现多跨度随机采样的效果。通过全面实验并与最先进的时序架构及当代基础模型进行比较，我们验证了ElasTST独特设计要素的有效性。研究结果表明，ElasTST为实际应用中的多时间跨度预测需求提供了鲁棒的解决方案。