DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition

Donghao Luo, Xue Wang

Keywords: Time series forecasting, Time series enhancement framework, Time series decomposition, Deep learning

TL;DR: We pioneer the idea of implicit decomposition and propose a powerful decomposition-based enhancement framework based on it, which can consistently enhance the forecasting performance of various state-of-the-art time series models.

Abstract:

In this paper, we pioneer the idea of implicit decomposition. And based on this idea, we propose a powerful decomposition-based enhancement framework, namely DecompNet. Our method converts the time series decomposition into an implicit process, where it can give a time series model the decomposition-related knowledge during inference, even though this model does not actually decompose the input time series. Thus, our DecompNet can enable a model to inherit the performance promotion brought by time series decomposition but will not introduce any additional inference costs, successfully enhancing the model performance while enjoying better efficiency. Experimentally, our DecompNet exhibits promising enhancement capability and compelling framework generality. Especially, it can also enhance the performance of the latest and state-of-the-art models, greatly pushing the performance limit of time series forecasting. Through comprehensive comparisons, DecompNet also shows excellent performance and efficiency superiority, making the decomposition-based enhancement framework surpass the well-recognized normalization-based frameworks for the first time. Code is
available at this repository: https://github.com/luodhhh/DecompNet.

Primary Area: Other (please use sparingly, only use the keyword field for more details)

Submission Number: 7198