Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning
Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning
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DOI：
10.24963/ijcai.2023/414
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
A. Galib;Andrew McDonald;Pang-Ning Tan;L. Luo
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文献类型：
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作者：
A. Galib;Andrew McDonald;Pang-Ning Tan;L. Luo
由于难以推断目标变量的尾部分布，预测未来时间窗口的块最大值是一项具有挑战性的任务。由于仅凭历史观测数据可能不足以训练鲁棒模型来预测块极大值，因此在许多科学领域中，域驱动过程模型通常可用于补充观测数据并提高预测精度。不幸的是，耦合的历史观察与过程模型输出是一个挑战，由于其不同的时间覆盖范围。本文介绍了Self-Recover，这是一个深度学习框架，通过采用自监督学习来预测时间窗口的块最大值，以解决变化的时间数据覆盖问题。具体来说，Self-Recover使用对比和生成式自监督学习方案的组合，沿着去噪自动编码器来估算缺失值。该框架还通过残差学习方法将历史观测值的表示与过程模型输出相结合，并学习表征块最大值的广义极值（GEV）分布。这使得框架能够可靠地估计每个时间窗口的块最大值沿着置信区间。在真实世界数据集上的大量实验证明了Self-Recover与其他最先进的预测方法相比的优越性。
Forecasting the block maxima of a future time window is a challenging task due to the difficulty in inferring the tail distribution of a target variable. As the historical observations alone may not be sufficient to train robust models to predict the block maxima, domain-driven process models are often available in many scientific domains to supplement the observation data and improve the forecast accuracy. Unfortunately, coupling the historical observations with process model outputs is a challenge due to their disparate temporal coverage. This paper presents Self-Recover, a deep learning framework to predict the block maxima of a time window by employing self-supervised learning to address the varying temporal data coverage problem. Specifically Self-Recover uses a combination of contrastive and generative self-supervised learning schemes along with a denoising autoencoder to impute the missing values. The framework also combines representations of the historical observations with process model outputs via a residual learning approach and learns the generalized extreme value (GEV) distribution characterizing the block maxima values. This enables the framework to reliably estimate the block maxima of each time window along with its confidence interval. Extensive experiments on real-world datasets demonstrate the superiority of Self-Recover compared to other state-of-the-art forecasting methods.