Fine-grained Forecasting Models Via Gaussian Process Blurring Effect
This work addresses forecasting accuracy issues for time series applications, presenting an incremental improvement by integrating denoising techniques.
The paper tackles the challenge of time series forecasting by proposing an end-to-end forecast-blur-denoise framework that divides labor between forecasting coarse-grained behavior and denoising fine-grained behavior using a Gaussian Process model, resulting in improved accuracy for state-of-the-art models.
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.