  Accurate spatial-temporal traffic flow forecasting is crucial in aiding
traffic managers in implementing control measures and assisting drivers in
selecting optimal travel routes. Traditional deep-learning based methods for
traffic flow forecasting typically rely on historical data to train their
models, which are then used to make predictions on future data. However, the
performance of the trained model usually degrades due to the temporal drift
between the historical and future data. To make the model trained on historical
data better adapt to future data in a fully online manner, this paper conducts
the first study of the online test-time adaptation techniques for
spatial-temporal traffic flow forecasting problems. To this end, we propose an
Adaptive Double Correction by Series Decomposition (ADCSD) method, which first
decomposes the output of the trained model into seasonal and trend-cyclical
parts and then corrects them by two separate modules during the testing phase
using the latest observed data entry by entry. In the proposed ADCSD method,
instead of fine-tuning the whole trained model during the testing phase, a lite
network is attached after the trained model, and only the lite network is
fine-tuned in the testing process each time a data entry is observed. Moreover,
to satisfy that different time series variables may have different levels of
temporal drift, two adaptive vectors are adopted to provide different weights
for different time series variables. Extensive experiments on four real-world
traffic flow forecasting datasets demonstrate the effectiveness of the proposed
ADCSD method. The code is available at https://github.com/Pengxin-Guo/ADCSD.
