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Time series forecasting is a challenging task due to the existence of complexand dynamic temporal dependencies. This can lead to incorrect predictions byeven the best forecasting models. Using more training data is one way toimprove the accuracy, but this source is often limited. In contrast, we arebuilding on successful denoising approaches for image generation by advocatingfor an end-to-end forecasting and denoising paradigm.  We propose an end-to-end forecast-blur-denoise forecasting framework byencouraging a division of labors between the forecasting and the denoisingmodels. The initial forecasting model is directed to focus on accuratelypredicting the coarse-grained behavior, while the denoiser model focuses oncapturing the fine-grained behavior that is locally blurred by integrating aGaussian Process model. All three parts are interacting for the best end-to-endperformance. Our extensive experiments demonstrate that our proposed approachis able to improve the forecasting accuracy of several state-of-the-artforecasting models as well as several other denoising approaches.

Fine-grained Forecasting Models Via Gaussian Process Blurring Effect