TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress

• URL: http://arxiv.org/abs/2502.15901v1
• Date: Fri, 21 Feb 2025 19:40:22 GMT
• Title: TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress
• Authors: Onat Gungor, Amanda Sofie Rios, Nilesh Ahuja, Tajana Rosing,
• Abstract summary: Out-of-distribution (OOD) data is a fundamental challenge in the deployment of machine learning models.<n>This paper seeks to address this research gap by conducting a comprehensive analysis of modality-agnostic OOD detection algorithms.<n>Our results demonstrate that: 1) the majority of state-of-the-art OOD methods exhibit limited performance on time-series data, and 2) OOD methods based on deep feature modeling may offer greater advantages for time-series OOD detection.
• Score: 6.140648893673249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting out-of-distribution (OOD) data is a fundamental challenge in the deployment of machine learning models. From a security standpoint, this is particularly important because OOD test data can result in misleadingly confident yet erroneous predictions, which undermine the reliability of the deployed model. Although numerous models for OOD detection have been developed in computer vision and language, their adaptability to the time-series data domain remains limited and under-explored. Yet, time-series data is ubiquitous across manufacturing and security applications for which OOD is essential. This paper seeks to address this research gap by conducting a comprehensive analysis of modality-agnostic OOD detection algorithms. We evaluate over several multivariate time-series datasets, deep learning architectures, time-series specific data augmentations, and loss functions. Our results demonstrate that: 1) the majority of state-of-the-art OOD methods exhibit limited performance on time-series data, and 2) OOD methods based on deep feature modeling may offer greater advantages for time-series OOD detection, highlighting a promising direction for future time-series OOD detection algorithm development.

Related papers

• Can OOD Object Detectors Learn from Foundation Models? [56.03404530594071]
  Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models.
  arXiv  Detail & Related papers  (2024-09-08T17:28:22Z)
• Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection [17.85872085904999]
  We propose a novel end-to-end skeleton-based model called Skeleton-OOD.<n>Skeleton-OOD is committed to improving the effectiveness of OOD tasks while ensuring the accuracy of ID recognition.<n>Our findings underscore the effectiveness of classic OOD detection techniques in the context of skeleton-based action recognition tasks.
  arXiv  Detail & Related papers  (2024-05-31T05:49:37Z)
• Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations [1.8531577178922987]
  "Overconfidence" is an intrinsic property of certain neural network architectures, leading to poor OOD detection. We measure extreme activation values in the penultimate layer of neural networks and then leverage this proxy of overconfidence to improve on several OOD detection baselines. Compared to the baselines, our method often grants substantial improvements, with double-digit increases in OOD detection.
  arXiv  Detail & Related papers  (2024-05-21T10:14:50Z)
• EAT: Towards Long-Tailed Out-of-Distribution Detection [55.380390767978554]
  This paper addresses the challenging task of long-tailed OOD detection. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes. We propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes, and (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data.
  arXiv  Detail & Related papers  (2023-12-14T13:47:13Z)
• Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources [73.28967478098107]
  Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data. It is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerning OOD patterns. We propose a data generation-based learning method named Auxiliary Task-based OOD Learning (ATOL) that can relieve the mistaken OOD generation.
  arXiv  Detail & Related papers  (2023-11-06T16:26:52Z)
• Can Pre-trained Networks Detect Familiar Out-of-Distribution Data? [37.36999826208225]
  We study the effect of PT-OOD on the OOD detection performance of pre-trained networks. We find that the low linear separability of PT-OOD in the feature space heavily degrades the PT-OOD detection performance. We propose a unique solution to large-scale pre-trained models: Leveraging powerful instance-by-instance discriminative representations of pre-trained models.
  arXiv  Detail & Related papers  (2023-10-02T02:01:00Z)
• A Survey on Out-of-Distribution Detection in NLP [119.80687868012393]
  Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches.
  arXiv  Detail & Related papers  (2023-05-05T01:38:49Z)
• Out-of-distribution Detection with Implicit Outlier Transformation [72.73711947366377]
  Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection. We propose a novel OE-based approach that makes the model perform well for unseen OOD situations.
  arXiv  Detail & Related papers  (2023-03-09T04:36:38Z)
• Pseudo-OOD training for robust language models [78.15712542481859]
  OOD detection is a key component of a reliable machine-learning model for any industry-scale application. We propose POORE - POsthoc pseudo-Ood REgularization, that generates pseudo-OOD samples using in-distribution (IND) data. We extensively evaluate our framework on three real-world dialogue systems, achieving new state-of-the-art in OOD detection.
  arXiv  Detail & Related papers  (2022-10-17T14:32:02Z)
• How Useful are Gradients for OOD Detection Really? [5.459639971144757]
  Out of distribution (OOD) detection is a critical challenge in deploying highly performant machine learning models in real-life applications. We provide an in-depth analysis and comparison of gradient based methods for OOD detection. We propose a general, non-gradient based method of OOD detection which improves over previous baselines in both performance and computational efficiency.
  arXiv  Detail & Related papers  (2022-05-20T21:10:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.