Time series forecasting is a challenging task due to the existence of complex and dynamic temporal dependencies. This can lead to incorrect predictions by even the best forecasting models. Using more training data is one way to improve the accuracy, but this source is often limited. In contrast, we are building on successful denoising approaches for image generation by advocating for an end-to-end forecasting and denoising paradigm. We propose an end-to-end forecast-blur-denoise forecasting framework by encouraging a division of labors between the forecasting and the denoising models. The initial forecasting model is directed to focus on accurately predicting the coarse-grained behavior, while the denoiser model focuses on capturing the fine-grained behavior that is locally blurred by integrating a Gaussian Process model. All three parts are interacting for the best end-to-end performance. Our extensive experiments demonstrate that our proposed approach is able to improve the forecasting accuracy of several state-of-the-art forecasting models as well as several other denoising approaches.

翻译：时间序列预测由于存在复杂且动态的时间依赖性而极具挑战性，即便最优预测模型也可能产生错误预测。增加训练数据是提升精度的一种方式，但数据来源往往受限。与此不同，我们借鉴图像生成领域成功的去噪方法，倡导一种端到端的预测与去噪范式。我们提出端到端的"预测-模糊-去噪"预测框架，通过促进预测模型与去噪模型之间的任务分工来实现：初始预测模型专注于准确预测粗粒度行为，而去噪模型通过集成高斯过程模型，聚焦于捕捉被局部模糊的细粒度行为。三个模块协同交互以实现最优端到端性能。广泛实验表明，我们提出的方法能够有效提升多个先进预测模型及其他去噪方法的预测精度。