Network structure of multivariate time series.

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

<p>Abstract</p> <p>Background</p> <p>Climate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.</p> <p>Results</p> <p>Based on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.</p> <p>Conclusion</p> <p>Surveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.</p

New Model for Geospatial Coverages in JSON : Coverage Implementation Schema and Its Implementation With JavaScript

Map browsers currently in place present maps and geospatial information using common image formats such as JPEG or PNG, usually created from a service on demand. This is a clear approach for a simple visualization map browser but prevents the browser from modifying the visualization since the content of the image file represents the intensity of colors of each pixel. In a desktop GIS, a coverage dataset is an array of values quantifying a certain property in each pixel of a subdomain of the space. The standard used to describe and distribute coverages is called web coverage service (WCS). Traditionally, encoding of coverages was too complex for map browsers implemented in JavaScript, relegating the WCS to a data download, a process that creates a file that will be later used in a desktop GIS. The combination of a coverage implementation schema in JSON, binary arrays, and HTML5 canvas makes it possible that web map browsers can be directly implemented in JavaScript

Exploring Granger causality in dynamical systems modeling and performance monitoring

Data-driven approaches are becoming increasingly crucial for modeling and performance monitoring of complex dynamical systems. Such necessity stems from complex interactions among sub-systems and high dimensionality that render majority of rst-principle based methods insucient. This work explores the capability of a recently proposed probabilistic graphical modeling technique called spatiotemporal pattern network (STPN) in capturing Granger causality among observations in a dynamical system. In this context, we introduce the notion of Granger-STPN (G-STPN) inspired by the notion of Granger causality. We compare the metrics used in the two frameworks for increasing memory in a dynamical system, and show that the metric for G-STPN can be approximated by transfer entropy. We apply this new framework for anomaly detection and root cause analysis in a robotic platform

Motion velocity as a preattentive feature in cartographic symbolization

The presented study aims to examine the process of preattentive processing of dynamic point symbols used in cartographic symbology. More specifically, we explore different motion types of geometric symbols on a map together with various motion velocity distribution scales. The main hypothesis is that, in specific cases, motion velocity of dynamic point symbols is the feature that could be perceived preattentively on a map. In a controlled laboratory experiment, with 103 participants and eye tracking methods, we used administrative border maps with animated symbols. Participants’ task was to find and precisely identify the fastest changing symbol. It turned out that not every type of motion could be perceived preattentively even though the motion distribution scale did not change. The same applied to symbols’ shape. Eye movement analysis revealed that successful detection was closely related to the fixation on the target after initial preattentive vision. This confirms a significant role of the motion velocity distribution and the usage of symbols’ shape in cartographic design of animated maps

Correlated space formation for human whole-body motion primitives and descriptive word labels

AbstractThe motion capture technology has been improved, and widely used for motion analysis and synthesis in various fields, such as robotics, animation, rehabilitation, and sports engineering. A massive amount of captured human data has already been collected. These prerecorded motion data should be reused in order to make the motion analysis and synthesis more efficient. The retrieval of a specified motion data is a fundamental technique for the reuse. Imitation learning frameworks have been developed in robotics, where motion primitive data is encoded into parameters in stochastic models or dynamical systems. We have also been making research on encoding motion primitive data into Hidden Markov Models, which are referred to as “motion symbol”, and aiming at integrating the motion symbols with language. The relations between motions and words in natural language will be versatile and powerful to provide a useful interface for reusing motion data. In this paper, we construct a space of motion symbols for human whole body movements and a space of word labels assigned to those movements. Through canonical correlation analysis, these spaces are reconstructed such that a strong correlation is formed between movements and word labels. These spaces lead to a method for searching for movement data from a query of word labels. We tested our proposed approach on captured human whole body motion data, and its validity was demonstrated. Our approach serves as a fundamental technique for extracting the necessary movements from a database and reusing them

Expectation-Maximization Binary Clustering for Behavioural Annotation

We present a variant of the well sounded Expectation-Maximization Clustering algorithm that is constrained to generate partitions of the input space into high and low values. The motivation of splitting input variables into high and low values is to favour the semantic interpretation of the final clustering. The Expectation-Maximization binary Clustering is specially useful when a bimodal conditional distribution of the variables is expected or at least when a binary discretization of the input space is deemed meaningful. Furthermore, the algorithm deals with the reliability of the input data such that the larger their uncertainty the less their role in the final clustering. We show here its suitability for behavioural annotation of movement trajectories. However, it can be considered as a general purpose algorithm for the clustering or segmentation of multivariate data or temporal series.Comment: 34 pages main text including 11 (full page) figure