STErrorCopilot: A Visualization and Diagnosis Copilot on Traffic Forecasting Models

Xiuhuai Xie, Yayao Hong, Jiangyi Fang, Liyue Chen, Leye Wang, Zhengquan Li, Guofeng Luo, Cheng Wang, Longbiao Chen

Abstract: Spatio-temporal traffic prediction (STTP) plays a crucial role in the development of smart cities. Deep learning models have shown superior performance in traffic prediction, but their opacity and complexity of traffic data present challenges for researchers in tuning models. To tune models effectively, we propose a generalized error analysis pipeline and design a corresponding visualization system, STError-Copilot (Spatio-temporal Error Copilot). The pipeline analyzes multi-perspective spatio-temporal features to determine whether prediction errors originate from semantic or modeling levels, and subsequently tunes the model. STErrorCopilot provides a comprehensive data analysis solution, covering the entire workflow from data loading, processing and visualization to final tuning, delivering end-to-end services. We perform error analysis and tuning on two classic models using two real datasets, demonstrating that our method accurately identifies errors and provides appropriate tuning recommendations.

External IDs:dblp:conf/msn/XieHFCWLLWC24