Towards Invariant Time Series Forecasting in Smart Cities

arXiv:2405.05430v1 Announce Type: new
Abstract: 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 …

abstract advancement arxiv challenge cities cs.lg economic economic growth edge forecasting growth however integration landscape lies networks neural networks performance pivotal planning series smart smart cities sustainability technologies time series time series forecasting type urban urban planning web