  In the transformative landscape of smart cities, the integration of the
cutting-edge web technologies into time series forecasting presents a pivotal
opportunity to enhance urban planning, sustainability, and economic growth. The
advancement of deep neural networks has significantly improved forecasting
performance. However, a notable challenge lies in the ability of these models
to generalize well to out-of-distribution (OOD) time series data. The inherent
spatial heterogeneity and domain shifts across urban environments create
hurdles that prevent models from adapting and performing effectively in new
urban environments. To tackle this problem, we propose a solution to derive
invariant representations for more robust predictions under different urban
environments instead of relying on spurious correlation across urban
environments for better generalizability. Through extensive experiments on both
synthetic and real-world data, we demonstrate that our proposed method
outperforms traditional time series forecasting models when tackling domain
shifts in changing urban environments. The effectiveness and robustness of our
method can be extended to diverse fields including climate modeling, urban
planning, and smart city resource management.
