Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains. Previous representation learning-based TSF algorithms typically embrace a contrastive learning paradigm featuring segregated trend-periodicity representations. Yet, these methodologies disregard the inherent high-impact noise embedded within time series data, resulting in representation inaccuracies and seriously demoting the forecasting performance. To address this issue, we propose CLeaRForecast, a novel contrastive learning framework to learn high-purity time series representations with proposed sample, feature, and architecture purifying methods. More specifically, to avoid more noise adding caused by the transformations of original samples (series), transformations are respectively applied for trendy and periodic parts to provide better positive samples with obviously less noise. Moreover, we introduce a channel independent training manner to mitigate noise originating from unrelated variables in the multivariate series. By employing a streamlined deep-learning backbone and a comprehensive global contrastive loss function, we prevent noise introduction due to redundant or uneven learning of periodicity and trend. Experimental results show the superior performance of CLeaRForecast in various downstream TSF tasks.

翻译：时间序列预测（TSF）在现代社会中具有重要价值，覆盖众多领域。以往基于表示学习的TSF算法通常采用分离趋势-周期表示的对比学习范式。然而，这些方法忽略了时间序列数据中固有的高影响噪声，导致表示不准确，严重损害预测性能。为解决该问题，我们提出CLeaRForecast——一种新颖的对比学习框架，通过所提出的样本、特征与架构纯化方法，学习高纯度时间序列表示。具体而言，为避免原始样本（序列）变换引入更多噪声，分别对趋势和周期部分进行变换，以提供噪声明显更少的优质正样本。此外，我们引入通道独立训练方式，以减轻多变量序列中不相关变量产生的噪声。通过采用精简的深度学习主干网络与全面的全局对比损失函数，我们避免了因周期性与趋势的冗余或不均衡学习而引入噪声。实验结果表明，CLeaRForecast在各类下游TSF任务中均展现出卓越性能。