Fine-grained Forecasting Models Via Gaussian Process Blurring Effect
Sepideh Koohfar Laura Dietz
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.