Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramatic and rapid changes in the world environment, the assumption that data obey Independent Identically Distribution is undermined by the subsequent changes in data distribution, known as concept drift, leading to weak replicability and transferability of the model over unseen data. To address the issue, previous approaches typically retrain the model, forcing it to fit the most recent observed data. However, retraining is problematic in that it leads to model lag, consumption of resources, and model re-invalidation, causing the drift problem to be not well solved in realistic scenarios. In this study, we propose a new urban time series prediction model for the concept drift problem, which encodes the drift by considering the periodicity in the data and makes on-the-fly adjustments to the model based on the drift using a meta-dynamic network. Experiments on real-world datasets show that our design significantly outperforms state-of-the-art methods and can be well generalized to existing prediction backbones by reducing their sensitivity to distribution changes.

翻译：城市场景下，对可持续发展具有重要贡献的时间序列数据预测，作为智慧城市的核心任务已被广泛研究。然而，随着全球环境的剧烈快速变化，数据服从独立同分布假设被其后续分布变化所破坏，这种变化被称为概念漂移，导致模型在未见数据上的可复现性与可迁移性减弱。为解决该问题，现有方法通常通过重新训练模型，使其拟合最新观测数据。但重训练会导致模型滞后、资源消耗及模型重新失效等问题，使得现实场景中漂移问题未能得到有效解决。本研究针对概念漂移问题提出新型城市时间序列预测模型，通过考虑数据周期性对漂移进行编码，并利用元动态网络基于漂移对模型进行即时调整。真实数据集上的实验表明，本设计显著优于现有最优方法，且可通过降低模型对分布变化的敏感性，良好泛化至现有预测骨干网络。