Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
DOI:
https://doi.org/10.1609/aaai.v37i6.25914Keywords:
ML: Time-Series/Data Streams, DMKM: Mining of Spatial, Temporal or Spatio-Temporal DataAbstract
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (Conet), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-Conet framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable Conet learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available at https://github.com/weifantt/Dish-TS.Downloads
Published
2023-06-26
How to Cite
Fan, W., Wang, P., Wang, D., Wang, D., Zhou, Y., & Fu, Y. (2023). Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 37(6), 7522-7529. https://doi.org/10.1609/aaai.v37i6.25914
Issue
Section
AAAI Technical Track on Machine Learning I