TS2Vec-Ensemble：一种增强的自监督框架用于时间序列预测
TS2Vec-Ensemble: An Enhanced Self-Supervised Framework for Time Series Forecasting
AI总结：
TS2Vec-Ensemble通过融合预训练动态与显式时间特征，提升长跨度时间序列预测性能。
摘要：
自监督表示学习，特别是通过对比方法如TS2Vec，已经推动了时间序列数据分析的发展。然而，这些模型在预测任务中常常表现不佳，因为它们的目标函数更注重实例区分，而非捕捉对准确预测至关重要的确定性模式，如季节性和趋势。本文介绍了TS2Vec-Ensemble，一种新的混合框架，旨在弥合这一差距。我们的方法通过融合预训练TS2Vec编码器中强大的隐式学习动态，与显式工程化的时间特征（编码周期性循环）来增强。这种融合是通过双模型集成架构实现的，其中两个不同的回归头——一个专注于学习动态，另一个专注于季节性模式——通过自适应加权方案结合。集成权重独立优化于每个预测时间跨度，使模型能够根据需要动态优先考虑短期动态或长期季节性。我们在ETT基准数据集上进行了广泛的实验，涵盖单变量和多变量预测。结果表明，TS2Vec-Ensemble在所有情况下均显著优于标准TS2Vec基线和其他最先进的模型，验证了我们的假设：学习表示与显式时间先验的结合是长跨度时间序列预测的更优策略。
英文摘要：
Self-supervised representation learning, particularly through contrastive methods like TS2Vec, has advanced the analysis of time series data. However, these models often falter in forecasting tasks because their objective functions prioritize instance discrimination over capturing the deterministic patterns, such as seasonality and trend, that are critical for accurate prediction. This paper introduces TS2Vec-Ensemble, a novel hybrid framework designed to bridge this gap. Our approach enhances the powerful, implicitly learned dynamics from a pretrained TS2Vec encoder by fusing them with explicit, engineered time features that encode periodic cycles. This fusion is achieved through a dual-model ensemble architecture, where two distinct regression heads -- one focused on learned dynamics and the other on seasonal patterns -- are combined using an adaptive weighting scheme. The ensemble weights are optimized independently for each forecast horizon, allowing the model to dynamically prioritize short-term dynamics or long-term seasonality as needed. We conduct extensive experiments on the ETT benchmark datasets for both univariate and multivariate forecasting. The results demonstrate that TS2Vec-Ensemble consistently and significantly outperforms the standard TS2Vec baseline and other state-of-the-art models, validating our hypothesis that a hybrid of learned representations and explicit temporal priors is a superior strategy for long-horizon time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
