Accurate forecasting of electric vehicle (EV) charging demand is critical for intelligent energy management and sustainable urban mobility. However, existing methods often fall short in modeling the multimodal complexity of urban systems, where dynamic pricing interventions, exogenous signals, and latent functional semantics interact in non-trivial ways. In this paper, we propose a novel explainable multimodal framework, MCKNet, that leverages Kolmogorov-Arnold Networks (KANs) to capture non-linear demand patterns while providing interpretable insights into cross-modal interactions. MCKNet consists of three key components: a temporal-intervention branch that disentangles proactive pricing effects, a semantic prototyping module that aligns stations with latent functional roles, and a conflict-aware evidential fusion mechanism that models uncertainty and resolves modal inconsistencies. Experimental results on real-world urban datasets demonstrate that MCKNet outperforms state-of-the-art baselines in forecasting accuracy, robustness, and generalizability across cities. Furthermore, our framework provides transparent visual interpretations of spatial, temporal, and semantic factors that drive EV charging behavior.

In time series analysis, disentangling long-term trends and seasonal patterns is crucial for capturing multi-scale temporal structures and improving both interpretability and forecasting accuracy. Recently, 2D modeling techniques have been incorporated into multivariate forecasting frameworks to better exploit periodic patterns. However, conventional decomposition methods often rely on simplistic moving averages that obscure critical patterns, while 2D modeling may entangle global trends with local variations and fail to normalize seasonal amplitudes-ultimately impairing both interpretability and forecast accuracy. To overcome these limitations, we propose XDNet (Exponential-Dimensional Network), a principled forecasting framework that explicitly disentangles trend and seasonal dynamics. At its core lies the Exponentially Weighted Decomposition (XWD), which applies decaying weights to past observations to preserve the integrity of long-term trends while adaptively normalizing seasonal fluctuations. The trend component is modeled using Temporal Kolmogorov-Arnold Networks (KAN) to capture intricate nonlinear dynamics, while the seasonal component is processed through a refined Inception-based module that robustly extracts fine-grained periodic dependencies. Extensive experiments on multiple benchmark datasets demonstrate that XDNet achieves state-of-the-art forecasting performance, delivering up to a 2.79% improvement in average accuracy over leading baselines, particularly in long-horizon prediction tasks.

Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focuses on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17% improvement.