Towards Invariant Time Series Forecasting in Smart Cities
This work tackles domain shift problems for urban planners and smart city developers, though it appears incremental as it builds on existing deep learning approaches with a focus on invariant representations.
The paper addresses the challenge of time series forecasting models failing to generalize to out-of-distribution data in smart cities due to spatial heterogeneity and domain shifts, proposing a method that derives invariant representations to improve robustness and outperforms traditional models in experiments.
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.