Investigating Pattern Neurons in Urban Time Series Forecasting

Urban time series forecasting is crucial for smart city development and is key to sustainable urban management. Although urban time series models (UTSMs) are effective in general forecasting, they often overlook low-frequency events, such as holidays and extreme weather, leading to degraded performance in practical applications. In this paper, we first investigate how UTSMs handle these infrequent patterns from a neural perspective. Based on our findings, we propose Pattern Neuron guided Training (PN-Train), a novel training method that features (i) a perturbation-based detector to identify neurons responsible for low-frequency patterns in UTSMs, and (ii) a fine-tuning mechanism that enhances these neurons without compromising representation learning on high-frequency patterns. Empirical results demonstrate that PN-Train considerably improves forecasting accuracy for low-frequency events while maintaining high performance for highfrequency events. The code is available at https://github.com/cwang-n us/PN-Train.