  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.
