CLeaRForecast: Contrastive Learning of High-Purity Representations for
  Time Series Forecasting
Jiaxin Gao Yuxiao Hu Qinglong Cao Siqi Dai Yuntian Chen
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.