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

Deep Neural Networks (DNNs) are typically trained by backpropagation in a
batch learning setting, which requires the entire training data to be made
available prior to the learning task. This is not scalable for many real-world
scenarios where new data arrives sequentially in a stream form. We aim to
address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on
the fly in an online setting. Unlike traditional online learning that often
optimizes some convex objective function with respect to a shallow model (e.g.,
a linear/kernel-based hypothesis), ODL is significantly more challenging since
the optimization of the DNN objective function is non-convex, and regular
backpropagation does not work well in practice, especially for online learning
settings. In this paper, we present a new online deep learning framework that
attempts to tackle the challenges by learning DNN models of adaptive depth from
a sequence of training data in an online learning setting. In particular, we
propose a novel Hedge Backpropagation (HBP) method for online updating the
parameters of DNN effectively, and validate the efficacy of our method on
large-scale data sets, including both stationary and concept drifting
scenarios.

论文提出了一种在线深度学习框架，使用新提出的 Hedge Backpropagation 方法，实现了对来自稳态和概念漂移情况下的大规模数据集进行自适应深度的 DNN 模型学习。