A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new method assigns anomaly scores to features for better interpretation.
A new method optimizes anomaly scoring from score distribution to improve AD performance.
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in…
Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch. One problem with this approach is that the anomaly score of rare-normal sounds becomes hig…
Proposes a method to measure similarity between anomaly scores from different methods.
Improves anomaly detection with contaminated unlabeled data.
This paper evaluates anomaly detection methods for multivariate time series data.
Topological anomaly scores predict return curves in S&P 500 stocks
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
A new model combines VAE and GAN for better anomaly detection in imbalanced datasets.
MIAEAD detects anomalies in mixed data types.
Mitigates anomaly score imbalance in long-tailed distributions.
Improves interpretability of anomaly scores in GBRBM-based detection.
Proposes BA method for unbiased time series anomaly detection evaluation.
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
Paper tackles anomaly detection with missing causal knowledge.
TadGAN detects anomalies in time series data using GANs and LSTM.
Proposes a new model for online anomaly detection in multivariate time series.
Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the proba…
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with "known" number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary…
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is base…
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average -NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…
Improves relevancy of black-box anomaly detectors with user feedback.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on -point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variation…
OMASGAN generates anomalous samples on distribution boundary to improve anomaly detection.
Novel framework for contextual anomaly detection models uncertainty.
DTOR explains anomalies with rule-based explanations.
Real-time anomaly detection for edge streams using MIDAS and MIDAS-F.
CCE improves anomaly detection metrics by measuring both confidence and consistency.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
Bayesian method estimates contamination factor for unsupervised anomaly detection.
Two new scoring methods improve anomaly detection in Isolation Forest.
Python package 'nonconform' simplifies conformal anomaly detection.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
A new method for detecting anomalies in large, high-dimensional data streams using probabilistic forest models.
Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrusion detection, finance, security and flight safety. In this paper we propose new conformalized density- and distance-based anomaly detection…
Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods. In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time…
We tackle anomaly detection in sparse time series data.
Trimming helps in conformal prediction when it separates anomaly scores.
Reconstruction error is a prevalent score used to identify anomalous samples when data are modeled by generative models, such as (variational) auto-encoders or generative adversarial networks. This score relies on the assumption that normal samples are located on a manifold and all anomalous samples are located outside…
We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for anomaly scores. These maps suffer from artifacts generated by…
Proposes a method to detect anomalies in financial time series using PCA and neural networks.
T2IVAE detects anomalies in time series data with high accuracy.
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms that use …
Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal predicti…
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two e…