Prioritizing End-User Needs in Time Series Anomaly Detection Benchmarking with OrionBench

핵심 개념

OrionBench provides a user-centric benchmarking framework for unsupervised time series anomaly detection, addressing challenges faced by end-users in selecting and evaluating models.

초록

OrionBench is a continuously maintained benchmarking framework that offers standardized abstractions, extensibility, hyperparameter standardization, pipeline verification, and frequent releases. It addresses key pain points experienced by end-users in selecting and utilizing anomaly detection models.
Key points from the content include:

• Introduction of OrionBench as a user-centric benchmarking framework for unsupervised time series anomaly detection.
• Challenges faced by end-users in selecting and evaluating anomaly detection models.
• Features of OrionBench such as standardized abstractions, extensibility, hyperparameter standardization, pipeline verification, and continuous releases.
• Real-world scenarios demonstrating the value of OrionBench in guiding researchers and providing confidence to end-users in selecting models.
• Progression of benchmarks over time showcasing stability and performance changes across different releases.

통계

"We propose OrionBench – a user centric continuously maintained benchmark for unsupervised time series anomaly detection."
"The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization, pipeline verification."
"Moreover, we walk through two real scenarios we experienced with OrionBench that highlight the importance of continuous benchmarks in unsupervised time series anomaly detection."

인용구

"We propose OrionBench – a user centric continuously maintained benchmark for unsupervised time series anomaly detection."
"The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization."
"Moreover, we walk through two real scenarios we experienced with OrionBench that highlight the importance of continuous benchmarks in unsupervised time series anomaly detection."

핵심 통찰 요약

Making the End-User a Priority in Benchmarking

by Sarah Alnegh... 게시일 arxiv.org 03-06-2024

https://arxiv.org/pdf/2310.17748.pdf

더 깊은 질문

How can benchmarking frameworks like OrionBench adapt to evolving machine learning techniques

Benchmarking frameworks like OrionBench can adapt to evolving machine learning techniques by continuously integrating new pipelines and datasets. By providing a standardized framework that allows for the seamless addition of new models, OrionBench ensures that researchers and end-users have access to the latest advancements in anomaly detection. This adaptability enables the benchmark to stay relevant in a rapidly changing field where new algorithms and methods are constantly being developed.

What are the implications of relying on benchmarks for end-users' decision-making processes

Relying on benchmarks for end-users' decision-making processes has several implications. Firstly, benchmarks provide a standardized way to compare different models, helping users make informed choices based on performance metrics. However, there is a risk of over-reliance on benchmarks as they may not always capture all aspects of model performance or be representative of real-world scenarios. End-users need to understand the limitations of benchmarks and consider other factors such as dataset characteristics and specific use cases when making decisions.

How can the industry leverage user-centric benchmarks like OrionBench to drive innovation in anomaly detection

The industry can leverage user-centric benchmarks like OrionBench to drive innovation in anomaly detection by fostering collaboration between researchers, developers, and end-users. By providing a platform where new models can be integrated, tested, and compared against existing ones, OrionBench encourages continuous improvement in algorithm development. End-users benefit from having access to state-of-the-art models that have been rigorously evaluated through benchmarking processes, leading to more effective solutions for detecting anomalies in time series data across various domains.