Detecting abrupt changes in real-time data streams from scientific
simulations presents a challenging task, demanding the deployment of accurate
and efficient algorithms. Identifying change points in live data stream
involves continuous scrutiny of incoming observations for deviations in their
statistical characteristics, particularly in high-volume data scenarios.
Maintaining a balance between sudden change detection and minimizing false
alarms is vital. Many existing algorithms for this purpose rely on known
probability distributions, limiting their feasibility. In this study, we
introduce the Kernel-based Cumulative Sum (KCUSUM) algorithm, a non-parametric
extension of the traditional Cumulative Sum (CUSUM) method, which has gained
prominence for its efficacy in online change point detection under less
restrictive conditions. KCUSUM splits itself by comparing incoming samples
directly with reference samples and computes a statistic grounded in the
Maximum Mean Discrepancy (MMD) non-parametric framework. This approach extends
KCUSUM's pertinence to scenarios where only reference samples are available,
such as atomic trajectories of proteins in vacuum, facilitating the detection
of deviations from the reference sample without prior knowledge of the data's
underlying distribution. Furthermore, by harnessing MMD's inherent random-walk
structure, we can theoretically analyze KCUSUM's performance across various use
cases, including metrics like expected delay and mean runtime to false alarms.
Finally, we discuss real-world use cases from scientific simulations such as
NWChem CODAR and protein folding data, demonstrating KCUSUM's practical
effectiveness in online change point detection.

通过引入基于核的累加和（KCUSUM）算法，我们扩展了传统累加和（CUSUM）方法，使之具备了针对高容量数据场景下在线变点检测的能力，并在仅有参考样本的情况下，通过与参考样本的比较来检测数据中与参考样本的偏离情况，从而在实时数据流中检测突变。

一种基于 KCUSUM 的实时自适应采样变点检测算法评估

An Evaluation of Real-time Adaptive Sampling Change Point Detection  Algorithm using KCUSUM

Time-series forecasting models often encounter abrupt changes in a given
period of time which generally occur due to unexpected or unknown events.
Despite their scarce occurrences in the training set, abrupt changes incur loss
that significantly contributes to the total loss. Therefore, they act as noisy
training samples and prevent the model from learning generalizable patterns,
namely the normal states. Based on our findings, we propose a reweighting
framework that down-weights the losses incurred by abrupt changes and
up-weights those by normal states. For the reweighting framework, we first
define a measurement termed Local Discrepancy (LD) which measures the degree of
abruptness of a change in a given period of time. Since a training set is
mostly composed of normal states, we then consider how frequently the temporal
changes appear in the training set based on LD. Our reweighting framework is
applicable to existing time-series forecasting models regardless of the
architectures. Through extensive experiments on 12 time-series forecasting
models over eight datasets with various in-output sequence lengths, we
demonstrate that applying our reweighting framework reduces MSE by 10.1% on
average and by up to 18.6% in the state-of-the-art model.

本文提出了一种基于权重调整的时间序列预测框架，通过降低由突发变化造成的损失和提升正常状态下的损失来提高预测精度。通过对 12 种模型在 8 种数据集上的实验，证明该方法能够平均降低 MSE 10.1％，在最先进的模型上可以高达 18.6％。

局部差异密度下的深度不平衡时间序列预测

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

In the analysis of sequential data, the detection of abrupt changes is
important in predicting future changes. In this paper, we propose statistical
hypothesis tests for detecting covariance structure changes in locally smooth
time series modeled by Gaussian Processes (GPs). We provide theoretically
justified thresholds for the tests, and use them to improve Bayesian Online
Change Point Detection (BOCPD) by confirming statistically significant changes
and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple
structural breaks in GPs even when hyperparameters are not tuned precisely. We
also provide conditions under which CBOCPD provides the lower prediction error
compared to BOCPD. Experimental results on synthetic and real-world datasets
show that our new tests correctly detect changes in the covariance structure in
GPs. The proposed algorithm also outperforms existing methods for the
prediction of nonstationarity in terms of both regression error and log
likelihood.

文章提出了一种用于检测局部平滑时间序列的协方差结构变化的统计假设检验方法，称为 Confirmatory BOCPD (CBOCPD)，该方法通过确认统计显著性的变化和非变化来改善 Bayesian Online Change Point Detection (BOCPD) 算法，实验结果表明该算法产生了比现有方法更低的预测误差和更高的对数似然性。