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 $33.3\%$.

在线更新时间序列预测模型针对概念漂移通过基于流数据调整预测模型来解决挑战。我们提出了一种新颖的方法 D3A，首先检测概念漂移，然后在检测到漂移后积极调整当前模型以适应漂移的概念，从而快速适应。通过在现有的训练样例中引入高斯噪声的数据增强策略，我们提出了一种最大程度利用历史数据进行模型适应的方法。我们的实证研究跨越六个数据集，证明了 D3A 在提高模型适应能力方面的有效性。与简单的时间卷积网络（TCN）基准相比，D3A 将均方误差（MSE）平均降低了 43.9%。对于最先进的模型，MSE 降低了 33.3%。

解决在线时间序列预测中的概念漂移问题：检测然后适应

Addressing Concept Shift in Online Time Series Forecasting:  Detect-then-Adapt

Online updating of time series forecasting models aims to address the concept
drifting problem by efficiently updating forecasting models based on streaming
data. Many algorithms are designed for online time series forecasting, with
some exploiting cross-variable dependency while others assume independence
among variables. Given every data assumption has its own pros and cons in
online time series modeling, we propose \textbf{On}line \textbf{e}nsembling
\textbf{Net}work (OneNet). It dynamically updates and combines two models, with
one focusing on modeling the dependency across the time dimension and the other
on cross-variate dependency. Our method incorporates a reinforcement
learning-based approach into the traditional online convex programming
framework, allowing for the linear combination of the two models with
dynamically adjusted weights. OneNet addresses the main shortcoming of
classical online learning methods that tend to be slow in adapting to the
concept drift. Empirical results show that OneNet reduces online forecasting
error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA)
method. The code is available at https://github.com/yfzhang114/OneNet.

在线更新时间序列预测模型，解决了概念漂移问题，提出了 OnNet 方法，动态地更新和组合两个模型，一个模型关注时间维度上的依赖性，另一个模型关注跨变量依赖性，并通过强化学习的方法实现两个模型的线性组合，动态调整权重，显著减少了在线预测误差。