Addressing Concept Shift in Online Time Series Forecasting:
  Detect-then-Adapt
YiFan Zhang Weiqi Chen Zhaoyang Zhu Dalin Qin Liang Sun Xue Wang Qingsong Wen Zhang Zhang Liang Wang Rong Jin
Online updating of time series forecasting models aims to tackle the
challenge of concept drifting by adjusting forecasting models based on
streaming data. While numerous algorithms have been developed, most of them
focus on model design and updating. In practice, many of these methods struggle
with continuous performance regression in the face of accumulated concept
drifts over time. To address this limitation, we present a novel approach,
Concept \textbf{D}rift \textbf{D}etection an\textbf{D} \textbf{A}daptation
(D3A), that first detects drifting conception and then aggressively adapts the
current model to the drifted concepts after the detection for rapid adaption.
To best harness the utility of historical data for model adaptation, we propose
a data augmentation strategy introducing Gaussian noise into existing training
instances. It helps mitigate the data distribution gap, a critical factor
contributing to train-test performance inconsistency. The significance of our
data augmentation process is verified by our theoretical analysis. Our
empirical studies across six datasets demonstrate the effectiveness of D3A in
improving model adaptation capability. Notably, compared to a simple Temporal
Convolutional Network (TCN) baseline, D3A reduces the average Mean Squared
Error (MSE) by $43.9\%$. For the state-of-the-art (SOTA) model, the MSE is
reduced by $33.3\%$.