MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Zekun Cai Renhe Jiang Xinyu Yang Zhaonan Wang Diansheng Guo Hiroki Kobayashi Xuan Song Ryosuke Shibasaki
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.