Wei Chen

Kang Luo, Yuanshao Zhu, Wei Chen et al.

Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore the confounding effects of geospatial context, leading to the acquisition of spurious correlations and limited generalization capabilities. To bridge this gap, we initially formulate a Structural Causal Model (SCM) to decipher the trajectory representation learning process from a causal perspective. Building upon the SCM, we further present a Trajectory modeling framework (TrajCL) based on Causal Learning, which leverages the backdoor adjustment theory as an intervention tool to eliminate the spurious correlations between geospatial context and trajectories. Extensive experiments on two real-world datasets verify that TrajCL markedly enhances performance in trajectory classification tasks while showcasing superior generalization and interpretability.

Wei Chen, Yuxuan Liang

The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks have achieved success in modeling various static spatio-temporal forecasting scenarios, real-world spatio-temporal data are typically received in a streaming manner, and the network continuously expands with the installation of new sensors. Thus, spatio-temporal forecasting in streaming scenarios faces dual challenges: the inefficiency of retraining models over newly arrived data and the detrimental effects of catastrophic forgetting over long-term history. To address these challenges, we propose a novel prompt tuning-based continuous forecasting method, following two fundamental tuning principles guided by empirical and theoretical analysis: expand and compress, which effectively resolve the aforementioned problems with lightweight tuning parameters. Specifically, we integrate the base spatio-temporal graph neural network with a continuous prompt pool, utilizing stored prompts (i.e., few learnable parameters) in memory, and jointly optimize them with the base spatio-temporal graph neural network. This method ensures that the model sequentially learns from the spatio-temporal data stream to accomplish tasks for corresponding periods. Extensive experimental results on multiple real-world datasets demonstrate the multi-faceted superiority of our method over the state-of-the-art baselines, including effectiveness, efficiency, universality, etc.

Wei Chen, Yuqian Wu, Yuanshao Zhu et al.

Spatio-temporal forecasting aims to predict the future state of dynamic systems and plays an important role in multiple fields. However, existing solutions only focus on modeling using a limited number of observed target variables. In real-world scenarios, exogenous variables can be integrated into the model as additional input features and associated with the target signal to promote forecast accuracy. Although promising, this still encounters two challenges: the inconsistent effects of different exogenous variables to the target system, and the imbalance effects between historical variables and future variables. To address these challenges, this paper introduces \model, a novel framework for modeling \underline{exo}genous variables in \underline{s}patio-\underline{t}emporal forecasting, which follows a ``select, then balance'' paradigm. Specifically, we first construct a latent space gated expert module, where fused exogenous information is projected into a latent space to dynamically select and recompose salient signals via specialized sub-experts. Furthermore, we design a siamese network architecture in which recomposed representations of past and future exogenous variables are fed into dual-branch spatio-temporal backbones to capture dynamic patterns. The outputs are integrated through a context-aware weighting mechanism to achieve dynamic balance during the modeling process. Extensive experiments on real-world datasets demonstrate the effectiveness, generality, robustness, and efficiency of our proposed framework.

Wei Chen, Yuxuan Liang

Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal anomalies, noise, and distributional shifts. Existing solutions primarily enhance robustness by modifying network architectures or training procedures. Nevertheless, these approaches are computationally intensive and resource-demanding, especially for large-scale applications. In this paper, we explore a novel test-time computing paradigm, namely learning with calibration, ST-TTC, for spatio-temporal forecasting. Through learning with calibration, we aim to capture periodic structural biases arising from non-stationarity during the testing phase and perform real-time bias correction on predictions to improve accuracy. Specifically, we first introduce a spectral-domain calibrator with phase-amplitude modulation to mitigate periodic shift and then propose a flash updating mechanism with a streaming memory queue for efficient test-time computation. ST-TTC effectively bypasses complex training-stage techniques, offering an efficient and generalizable paradigm. Extensive experiments on real-world datasets demonstrate the effectiveness, universality, flexibility and efficiency of our proposed method.