Given the increasing amount and general complexity of time series data in
domains such as finance, weather forecasting, and healthcare, there is a
growing need for state-of-the-art performance models that can provide
interpretable insights into underlying patterns and relationships. Attribution
techniques enable the extraction of explanations from time series models to
gain insights but are hard to evaluate for their robustness and
trustworthiness. We propose the Attribution Stability Indicator (ASI), a
measure to incorporate robustness and trustworthiness as properties of
attribution techniques for time series into account. We extend a perturbation
analysis with correlations of the original time series to the perturbed
instance and the attributions to include wanted properties in the measure. We
demonstrate the wanted properties based on an analysis of the attributions in a
dimension-reduced space and the ASI scores distribution over three whole time
series classification datasets.

我们提出了 Attribution Stability Indicator (ASI) 作为一种度量、考虑鲁棒性和可靠性的时间序列属性解释技术的方法。通过在原始时间序列和属性之间引入扰动分析和相关性，我们展示了这种方法的可靠性和属性，并在三个时间序列分类数据集上的 ASI 分数分布中进行了分析。

引入归属稳定指数：一种用于时间序列 XAI 归属的测量方法

Introducing the Attribution Stability Indicator: a Measure for Time  Series XAI Attributions

Selecting the right resources for big data analytics jobs is hard because of
the wide variety of configuration options like machine type and cluster size.
As poor choices can have a significant impact on resource efficiency, cost, and
energy usage, automated approaches are gaining popularity. Most existing
methods rely on profiling recurring workloads to find near-optimal solutions
over time. Due to the cold-start problem, this often leads to lengthy and
costly profiling phases. However, big data analytics jobs across users can
share many common properties: they often operate on similar infrastructure,
using similar algorithms implemented in similar frameworks. The potential in
sharing aggregated profiling runs to collaboratively address the cold start
problem is largely unexplored.
We present Karasu, an approach to more efficient resource configuration
profiling that promotes data sharing among users working with similar
infrastructures, frameworks, algorithms, or datasets. Karasu trains lightweight
performance models using aggregated runtime information of collaborators and
combines them into an ensemble method to exploit inherent knowledge of the
configuration search space. Moreover, Karasu allows the optimization of
multiple objectives simultaneously. Our evaluation is based on performance data
from diverse workload executions in a public cloud environment. We show that
Karasu is able to significantly boost existing methods in terms of performance,
search time, and cost, even when few comparable profiling runs are available
that share only partial common characteristics with the target job.

Karasu 是一种更高效的资源配置剖析方法，通过促进类似基础设施、框架、算法或数据集的用户之间的数据共享，训练轻量级性能模型并将它们组合成一个集成方法，以开发配置搜索空间中固有的知识，同时优化多个目标，从而显著提升现有方法在性能、搜索时间和成本方面的表现。