  Time series forecasting has widespread applications in urban life ranging
from air quality monitoring to traffic analysis. However, accurate time series
forecasting is challenging because real-world time series suffer from the
distribution shift problem, where their statistical properties change over
time. Despite extensive solutions to distribution shifts in domain adaptation
or generalization, they fail to function effectively in unknown,
constantly-changing distribution shifts, which are common in time series. In
this paper, we propose Hyper Time- Series Forecasting (HTSF), a
hypernetwork-based framework for accurate time series forecasting under
distribution shift. HTSF jointly learns the time-varying distributions and the
corresponding forecasting models in an end-to-end fashion. Specifically, HTSF
exploits the hyper layers to learn the best characterization of the
distribution shifts, generating the model parameters for the main layers to
make accurate predictions. We implement HTSF as an extensible framework that
can incorporate diverse time series forecasting models such as RNNs and
Transformers. Extensive experiments on 9 benchmarks demonstrate that HTSF
achieves state-of-the-art performances.
