Due to non-stationarity of time series, the distribution shift problem
largely hinders the performance of time series forecasting. Existing solutions
either fail for the shifts beyond simple statistics or the limited
compatibility with forecasting models. In this paper, we propose a general
decoupled formulation for time series forecasting, with no reliance on fixed
statistics and no restriction on forecasting architectures. Then, we make such
a formulation formalized into a bi-level optimization problem, to enable the
joint learning of the transformation (outer loop) and forecasting (inner loop).
Moreover, the special requirements of expressiveness and bi-direction for the
transformation motivate us to propose instance normalization flows (IN-Flow), a
novel invertible network for time series transformation. Extensive experiments
demonstrate our method consistently outperforms state-of-the-art baselines on
both synthetic and real-world data.

本文提出了一种新的时间序列预测方法，通过解决分布转移问题以及采用解耦的公式，并在联合学习变换和预测的基础上，利用 instance normalization flows 实现时间序列的转换。通过广泛实验验证，在合成和实际数据上，我们的方法始终优于现有基线算法。