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1. MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives

   https://doi.org/10.1109/ICDM50108.2020.00147  

   Nakamura, Takaaki; Imamura, Makoto; Mercer, Ryan; Keogh, Eamonn (November 2020, ICDM 2020)

   null (Ed.)

   Time series anomaly detection remains a perennially important research topic. If anything, it is a task that has become increasingly important in the burgeoning age of IoT. While there are hundreds of anomaly detection methods in the literature, one definition, time series discords, has emerged as a competitive and popular choice for practitioners. Time series discords are subsequences of a time series that are maximally far away from their nearest neighbors. Perhaps the most attractive feature of discords is their simplicity. Unlike many parameter laden methods, discords require only a single parameter to be set by the user: the subsequence length. In this work we argue that the utility of discords is reduced by sensitivity to this single user choice. The obvious solution to this problem, computing discords of all lengths then selecting the best anomalies (under some measure), seems to be computationally untenable. However, in this work we introduce MERLIN, an algorithm that can efficiently and exactly find discords of all lengths in massive time series archives. 

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2. Auto Annotation of Linguistic Features for Audio Deepfake Discernment

   https://doi.org/10.1609/aaaiss.v2i1.27682  

   Nwosu, Kifekachukwu; Evered, Chloe; Khanjani, Zahra; Bhalli, Noshaba; Davis, Lavon; Mallinson, Christine; Janeja, Vandana P. (January 2024, Proceedings of the AAAI Symposium Series)

   We present an innovative approach to auto-annotate Expert Defined Linguistic Features (EDLFs) as subsequences in audio time series to improve audio deepfake discernment. In our prior work, these linguistic features – namely pitch, pause, breath, consonant release bursts, and overall audio quality, labeled by experts on the entire audio signal – have been shown to improve detection of audio deepfakes with AI algorithms. We now expand our approach to pilot a way to auto annotate subsequences in the time series that correspond to each EDLF. We developed an ensemble of discords, i.e. anomalies in time series, detected using matrix profiles across multiple discord lengths to identify multiple types of EDLFs. Working closely with linguistic experts, we evaluated where discords overlapped with EDLFs in the audio signal data. Our ensemble method to detect discords across multiple discord lengths achieves much higher accuracy than using individual discord lengths to detect EDLFs. With this approach and domain validation we establish the feasibility of using time series subsequences to capture EDLFs to supplement annotation by domain experts, for improved audio deepfake detection. 

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3. Introducing the contrast profile: a novel time series primitive that allows real world classification

   https://doi.org/https://doi.org/10.1007/s10618-022-00824-5  

   Ryan Mercer, Sara Alaee (March 2022, Data mining and knowledge discovery)

   Editor in Chief: Johannes Fürnkranz (Ed.)

   Time series data remains a perennially important datatype considered in data mining. In the last decade there has been an increasing realization that time series data can be best understood by reasoning about time series subsequences on the basis of their similarity to other subsequences: the two most familiar such time series concepts being motifs and discords. Time series motifs refer to two particularly close subsequences, whereas time series discords indicate subsequences that are far from their nearest neighbors. However, we argue that it can sometimes be useful to simultaneously reason about a subsequence’s closeness to certain data and its distance to other data. In this work we introduce a novel primitive called the Contrast Profile that allows us to efficiently compute such a definition in a principled way. As we will show, the Contrast Profile has many downstream uses, including anomaly detection, data exploration, and preprocessing unstructured data for classification. We demonstrate the utility of the Contrast Profile by showing how it allows end-to-end classification in datasets with tens of billions of datapoints, and how it can be used to explore datasets and reveal subtle patterns that might otherwise escape our attention. Moreover, we demonstrate the generality of the Contrast Profile by presenting detailed case studies in domains as diverse as seismology, animal behavior, and cardiology. 

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4. Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

   https://doi.org/10.3390/electronics12091971  

   Mozaffari, Mahsa; Doshi, Keval; Yilmaz, Yasin (May 2023, Electronics)

   In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. 

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5. An Online Algorithm for Detecting Anomalies using Fuzzy Clustering

   Darrin M. Hanna, Michael F. (August 2018, Proceedings of the International Conference on Artificial Intelligence, Las Vegas, NV)

   A fuzzy clustering algorithm with the ability to learn unsupervised can be used to detect objects of interest in semi-structured data. An online application of a fuzzy clustering algorithm with merging was implemented in both software and hardware to test anomaly detection in atomic force microscopy (AFM). The requisite components of the algorithm were all estimated, measured, and verified to meet real time constraints for incoming data. After clusters have been formed, representing the background of an image, any new cluster is an abnormality to the surface which is of interest to the user. This real-time detection of anomalies is important for identifying regions of interest for faster and higher resolution scanning. Results show this application is successfully capable of detecting anomalies in AFM topographic images. The approach taken in this paper is generic and can be applied to other applications with a continuous data stream. 

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