标题： ST-MTM：具有季节性趋势分解的掩码时间序列建模用于时间序列预测 显示英文标题

标题： ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting

摘要： 预测复杂的时序数据是一个重要但具有挑战性的问题，涉及各种工业应用。 最近，提出了一种掩码时序建模方法，通过从未被掩码的段中重建被掩码的段，以有效建模时间依赖性。 然而，由于时序中的语义信息涉及由多个时序组件生成的复杂时间变化，简单地掩码原始时序会忽略固有的语义结构，这可能导致MTM学习到原始数据中存在的虚假时间模式。 为了捕捉不同的时间语义，我们表明掩码建模技术应通过分解方法来处理纠缠模式。 具体来说，我们提出了ST-MTM，一种具有季节趋势分解的掩码时序建模框架，其中包括一种新的针对季节趋势成分的掩码方法，该方法结合了每个成分的不同时间变化。 ST-MTM对季节成分使用周期掩码策略，根据固有的多周期性生成多个掩码季节序列，并对趋势成分使用子序列掩码策略，以掩码共享相似变化的时间区域。 所提出的掩码方法为学习复杂的时间变化和依赖关系提供了一个有效的预训练任务。 此外， ST-MTM引入了一个对比学习任务，通过增强多个掩码季节表示之间的上下文一致性来支持掩码建模。 实验结果表明，与现有的掩码建模、对比学习和监督预测方法相比，我们提出的ST-MTM在预测性能上始终表现出色。 显示英文摘要

摘要： Forecasting complex time series is an important yet challenging problem that involves various industrial applications. Recently, masked time-series modeling has been proposed to effectively model temporal dependencies for forecasting by reconstructing masked segments from unmasked ones. However, since the semantic information in time series is involved in intricate temporal variations generated by multiple time series components, simply masking a raw time series ignores the inherent semantic structure, which may cause MTM to learn spurious temporal patterns present in the raw data. To capture distinct temporal semantics, we show that masked modeling techniques should address entangled patterns through a decomposition approach. Specifically, we propose ST-MTM, a masked time-series modeling framework with seasonal-trend decomposition, which includes a novel masking method for the seasonal-trend components that incorporates different temporal variations from each component. ST-MTM uses a period masking strategy for seasonal components to produce multiple masked seasonal series based on inherent multi-periodicity and a sub-series masking strategy for trend components to mask temporal regions that share similar variations. The proposed masking method presents an effective pre-training task for learning intricate temporal variations and dependencies. Additionally, ST-MTM introduces a contrastive learning task to support masked modeling by enhancing contextual consistency among multiple masked seasonal representations. Experimental results show that our proposed ST-MTM achieves consistently superior forecasting performance compared to existing masked modeling, contrastive learning, and supervised forecasting methods.