This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector SNN-CAD which is a parameter-light algorithm for on-line learning and anomaly detection with wellcalibrated false alarm rate.
We propose two parameterfree dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets.
Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning. This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection.
The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods. The increased societal need for surveillance and the decrease in cost of sensors have led to a number of new challenges. The problem is not to collect data but to use it effectively for decision support. Manual interpretation of huge amounts of data in real-time is not feasible; the operator of a surveillance system needs support to analyze and understand all incoming data.
In this paper an approach to intelligent video surveillance is presented, with emphasis on finding behavioural anomalies. Two different anomaly detection methods are compared and combined. The results show that it is possible to best increase the total detection performance by combining two different anomaly detectors rather than employing them independently.
A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and.
The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory.
Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context. Praise from early readers ver since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic.
In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades.
Peter Gr 0Wiley series in probability and statisticsnwald, CWI and University of Leiden hafer and Vovk have thoroughly re-written their book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance.
This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors. Ioannis Karatzas, Columbia University. The tech- cal program of ALT , contained 26 papers selected from 44 submissions and? The invited talks were presented in joint sessions of both conferences. ALT was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?
DS was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti? As is the tradition, it was co-located and held in parallel with Algorithmic Learning Theory. Proceedings by Marcus Hutter 2 editions published in in English and held by 23 WorldCat member libraries worldwide.
Game-theoretic probability : theory and applications to prediction, science, and finance by Glenn Shafer 1 edition published in in English and held by 16 WorldCat member libraries worldwide.
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Conformal prediction: future events
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Efficient iterative virtual screening with Apache Spark and conformal prediction
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