Novelty Detection And Cluster Analysis In Time Series Data Using Variational Autoencoder Feature Maps

The identification of atypical events and anomalies in complex data systems is an essential yet challenging task. The dynamic nature of these systems produces huge volumes of data that is often heterogeneous, and the failure to account for this will impede the detection of anomalies. Time series data encompass these issues and its high dimensional nature intensifies these challenges. This research presents a framework for the identification of anomalies in temporal data. A comparative analysis of Centroid, Density and Neural Network-based clustering techniques was performed and their scalability was assessed. This facilitated the development of a new algorithm called the Variational Autoencoder Feature Map (VAEFM) which is an ensemble method that is based on Kohonen’s Self-Organizing Maps (SOM) and Variational Autoencoders. The VAEFM is an unsupervised learning algorithm that models the distribution of temporal data without making a priori assumptions. It incorporates principles of novelty detection to enhance the representational capacity of SOMs neurons, which improves their ability to generalize with novel data. The VAEFM technique was demonstrated on a dataset of accumulated aircraft sensor recordings, to detect atypical events that transpired in the approach phase of flight. This is a proactive means of accident prevention and is therefore advantageous to the Aviation industry. Furthermore, accumulated aircraft data presents big data challenges, which requires scalable analytical solutions. The results indicated that VAEFM successfully identified temporal dependencies in the flight data and produced several clusters and outliers. It analyzed over 2500 flights in under 5 minutes and identified 12 clusters, two of which contained stabilized approaches. The remaining comprised of aborted approaches, excessively high/fast descent patterns and other contributory factors for unstabilized approaches. Outliers were detected which revealed oscillations in aircraft trajectories; some of which would have a lower detection rate using traditional flight safety analytical techniques. The results further indicated that VAEFM facilitates large-scale analysis and its scaling efficiency was demonstrated on a High Performance Computing System, by using an increased number of processors, where it achieved an average speedup of 70%

Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Applications of high-frequency telematics for driving behavior analysis

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsProcessing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy-making. A popular way of analyzing driving behavior is to move the focus to the maneuvers as they give useful information about the driver who is performing them. Previous research on maneuver detection can be divided into two strategies, namely, 1) using fixed thresholds in inertial measurements to define the start and end of specific maneuvers or 2) using features extracted from rolling windows of sensor data in a supervised learning model to detect maneuvers. While the first strategy is not adaptable and requires fine-tuning, the second needs a dataset with labels (which is time-consuming) and cannot identify maneuvers with different lengths in time. To tackle these shortcomings, we investigate a new way of identifying maneuvers from vehicle telematics data, through motif detection in time-series. Using a publicly available naturalistic driving dataset (the UAH-DriveSet), we conclude that motif detection algorithms are not only capable of extracting simple maneuvers such as accelerations, brakes, and turns, but also more complex maneuvers, such as lane changes and overtaking maneuvers, thus validating motif discovery as a worthwhile line for future research in driving behavior. We also propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. We test TripMD in the same UAH-DriveSet dataset and show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.Nas últimas décadas, o processamento e análise de dados de condução tem recebido um interesse cada vez maior, com aplicações que abrangem a área de seguros de automóveis até a atea de regulação. Tipicamente, a análise de condução compreende a extração e estudo de manobras uma vez que estas contêm informação relevante sobre a performance do condutor. A investigação prévia sobre este tema pode ser dividida em dois tipos de estratégias, a saber, 1) o uso de valores fixos de aceleração para definir o início e fim de cada manobra ou 2) a utilização de modelos de aprendizagem supervisionada em janelas temporais. Enquanto o primeiro tipo de estratégias é inflexível e requer afinação dos parâmetros, o segundo precisa de dados de condução anotados (o que é moroso) e não é capaz de identificar manobras de diferentes durações. De forma a mitigar estas lacunas, neste trabalho, aplicamos métodos desenvolvidos na área de investigação de séries temporais de forma a resolver o problema de deteção de manobras. Em particular, exploramos área de deteção de motifs em séries temporais e testamos se estes métodos genéricos são bem-sucedidos na deteção de manobras. Também propomos o TripMD, um sistema que extrai os padrões de condução mais relevantes de um conjuntos de viagens e fornece uma simples visualização. TripMD é testado num conjunto de dados públicos (o UAH-DriveSet), do qual concluímos que (1) o nosso sistema é capaz de extrair padrões de condução/manobras de um único condutor que estão relacionados com o perfil de condução do condutor em questão e (2) o nosso sistema pode ser usado para identificar o perfil de condução de um condutor desconhecido de um conjunto de condutores cujo comportamento nos é conhecido