On the Statistical Modeling and Analysis of Repairable Systems

We review basic modeling approaches for failure and maintenance data from repairable systems. In particular we consider imperfect repair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where several systems of the same kind are observed, we show how observed covariates and unobserved heterogeneity can be included in the models. We also consider various approaches to trend testing. Modern reliability data bases usually contain information on the type of failure, the type of maintenance and so forth in addition to the failure times themselves. Basing our work on recent literature we present a framework where the observed events are modeled as marked point processes, with marks labeling the types of events. Throughout the paper the emphasis is more on modeling than on statistical inference.Comment: Published at http://dx.doi.org/10.1214/088342306000000448 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spatio-temporal traffic anomaly detection for urban networks

Urban road networks are often affected by disruptions such as accidents and roadworks, giving rise to congestion and delays, which can, in turn, create a wide range of negative impacts to the economy, environment, safety and security. Accurate detection of the onset of traffic anomalies, specifically Recurrent Congestion (RC) and Nonrecurrent Congestion (NRC) in the traffic networks, is an important ITS function to facilitate proactive intervention measures to reduce the level of severity of congestion. A substantial body of literature is dedicated to models with varying levels of complexity that attempt to identify such anomalies. Given the complexity of the problem, however, very less effort is dedicated to the development of methods that attempt to detect traffic anomalies using spatio-temporal features. Driven both by the recent advances in deep learning techniques and the development of Traffic Incident Management Systems (TIMS), the aim of this research is to develop novel traffic anomaly detection models that can incorporate both spatial and temporal traffic information to detect traffic anomalies at a network level. This thesis first reviews the state of the art in traffic anomaly detection techniques, including the existing methods and emerging machine learning and deep learning methods, before identifying the gaps in the current understanding of traffic anomaly and its detection. One of the problems in terms of adapting the deep learning models to traffic anomaly detection is the translation of time series traffic data from multiple locations to the format necessary for the deep learning model to learn the spatial and temporal features effectively. To address this challenging problem and build a systematic traffic anomaly detection method at a network level, this thesis proposes a methodological framework consisting of (a) the translation layer (which is designed to translate the time series traffic data from multiple locations over the road network into a desired format with spatial and temporal features), (b) detection methods and (c) localisation. This methodological framework is subsequently tested for early RC detection and NRC detection. Three translation layers including connectivity matrix, geographical grid translation and spatial temporal translation are presented and evaluated for both RC and NRC detection. The early RC detection approach is a deep learning based method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The NRC detection, on the other hand, involves only the application of the CNN. The performance of the proposed approach is compared against other conventional congestion detection methods, using a comprehensive evaluation framework that includes metrics such as detection rates and false positive rates, and the sensitivity analysis of time windows as well as prediction horizons. The conventional congestion detection methods used for the comparison include Multilayer Perceptron, Random Forest and Gradient Boost Classifier, all of which are commonly used in the literature. Real-world traffic data from the City of Bath are used for the comparative analysis of RC, while traffic data in conjunction with incident data extracted from Central London are used for NRC detection. The results show that while the connectivity matrix may be capable of extracting features of a small network, the increased sparsity in the matrix in a large network reduces its effectiveness in feature learning compared to geographical grid translation. The results also indicate that the proposed deep learning method demonstrates superior detection accuracy compared to alternative methods and that it can detect recurrent congestion as early as one hour ahead with acceptable accuracy. The proposed method is capable of being implemented within a real-world ITS system making use of traffic sensor data, thereby providing a practically useful tool for road network managers to manage traffic proactively. In addition, the results demonstrate that a deep learning-based approach may improve the accuracy of incident detection and locate traffic anomalies precisely, especially in a large urban network. Finally, the framework is further tested for robustness in terms of network topology, sensor faults and missing data. The robustness analysis demonstrates that the proposed traffic anomaly detection approaches are transferable to different sizes of road networks, and that they are robust in the presence of sensor faults and missing data.Open Acces

Africa and the Millenium Developement Goals (MDGs): What´s Right, What's Wrong and What's Missing

The deadline of 2015 for the MDGs is getting dangerously close. It is well known that most African countries will not meet most MDGs set out in 2000 as an ambitious plan to achieve fast socio-economic progress in developing countries. This article introduces a special issue to the problematic of MDGs in Africa, progress achieved, challenges and what is missing from the MDG agenda. The article provides an overview of the situation with regards to the MDGs, with particular emphasis on the objective of reducing poverty, which is highly associated with the other MDGs. It is shown that the record in poverty reduction has been generally disappointing. Besides, the poverty reduction agenda has contributed to the fall of “grand narratives” of development and the demise of the idea of “development” as understood in the traditions of old development economics and political economy of development. The New Poverty and the MDG agendas have been relatively successful in garnering support to increase international assistance for basic needs in African countries, but are much less impressive in terms of achieved outcomes and their contribution to development strategies. The paper finally introduces the main contents of the special issue and some of the most salient critical points from a set of articles that critically engage with dominant discourses around MDGs in Africa

Debt Dynamics and Contingency Financing: Theoretical Reappraisal of the HIPC Initiative

Sovereign debt management, External debt, Foreign aid, Growth models, Economic development

An assessment of the livelihood vulnerability of the riverbank erosion hazard and its impact on food security for rural households in Bangladesh

As the effects of climate change and hazards are starting to be felt worldwide, there are certain frontline countries that are most at risk and Bangladesh is genuinely at risk in terms of its economic viability and food security unless its citizens develop adaptation strategies to compensate for these effects. This study analyses how the impacts of climate change and hazards (specifically riverbank erosion) are already jeopardising the livelihood and food security of rural riparian (riverbank and char) households in Bangladesh, compromising their access to arable land, and thereby holding back their potential for both sustenance and economic development. The researcher has conducted extensive research in two severe riverbank erosion-prone districts in Bangladesh to assess the severity of these problems and to seek the strategies the affected people deploy to offset the effects. This study takes a holistic approach to two key vulnerability assessment methods – the Livelihood Vulnerability Index (LVI) and the Climate Vulnerability Index (CVI). Importantly, this study also develops an indicator-based Resilience Capacity Index (RCI) in order to understand the factors influencing the resilience capacity of these households. This study reveals that the LVI and CVI values are different between char (sandbar) and riverbank communities: households inhabiting char lands display the most vulnerability to climate change and hazards. Also, riparian households are found to be vulnerable due to their relative inaccessibility and low livelihood status which, coupled with the impact of the climate on river morphology, are causing erosion and a loss of land with a consequent decrease in economic potential, thereby perpetuating a cycle of poverty. Creating employment opportunities, increasing the level of education and ensuring access to food, water and health services are potential strategies that are likely to enhance the resilience capacity of such vulnerable households in Bangladesh. In regards to food security, more than 50% of the households are in the ‘food insecure’ category, with a per capita calorie consumption of 12% less than the standard minimum daily requirement. The estimated low Food Security Index (FSI) value indicates that these households can usually manage food twice per day for their family members. The results of logit modelling indicate that household size, educational attainment, adoption of livestock and access to non-farm earnings are important determinants of household food security. This study also finds new evidence that suggests access to improved health care also needs policy support in parallel with improved access to food to achieve and to sustain long-term food security in Bangladesh. Properly targeted income transfers and credit programs along with infrastructure and human development programs in the erosion-affected areas across the country may have very high payoffs by improving food security, and thus, reducing poverty in the long-term. To build resilience, households are autonomously adopting adaptation strategies such as diversifying crops, tree plantation (generally by large and medium farmers), and homestead gardening and migration (generally by small and landless farmers). However, some important barriers to adaptation are felt heterogeneously among the farming groups: among these are access to credit and a lack of information on appropriate adaptation strategies. The results of multi-nominal logit modelling indicate that the choice of an adaptation strategy is influenced significantly by a household head’s education, household income, farm category, access to institutions and social capital. To support adaptation locally and to enhance households’ resilience to cope better with riverbank hazards and other climate change issues, government intervention through planned adaptation such as access to institutions, credit facilities and a package of technologies through agro-ecologically based research are required. This study has contributed to our knowledge base through tailoring various theories and approaches in the context of riparian households in Bangladesh. The innovative coping and adaptation strategies could provide new insights for households in other hazard-prone regions in the world. The analytical framework used for assessing vulnerability, resilience, household food security and adaptation strategies should be replicated in other countries having similar characteristics to Bangladesh that are experiencing adverse impacts from climate change

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Deep Learning Models for Irregularly Sampled and Incomplete Time Series

Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, geology, finance, and health. Such data present fundamental challenges to many classical models from machine learning and statistics. The first challenge with modeling such data is the presence of variable time gaps between the observation time points. The second challenge is that the dimensionality of the inputs can be different for different data cases. This occurs naturally due to the fact that different data cases are likely to include different numbers of observations. The third challenge is that different irregularly sampled instances have observations recorded at different times. This results in a lack of temporal alignment across data cases. There could also be a lack of alignment of observation time points across different dimensions in the same multivariate time series. These features of irregularly sampled time series data invalidate the assumption of a coherent fully-observed fixed-dimensional feature space that underlies many basic supervised and unsupervised learning models. In this thesis, we focus on the development of deep learning models for the problems of supervised and unsupervised learning from irregularly sampled time series data. We begin by introducing a computationally efficient architecture for whole time series classification and regression problems based on the use of a novel deterministic interpolation-based layer that acts as a bridge between multivariate irregularly sampled time series data instances and standard neural network layers that assume regularly-spaced or fixed-dimensional inputs. The architecture is based on the use of a radial basis function (RBF) kernel interpolation network followed by the application of a prediction network. Next, we show how the use of fixed RBF kernel functions can be relaxed through the use of a novel attention-based continuous-time interpolation framework. We show that using attention to learn temporal similarity results in improvements over fixed RBF kernels and other recent approaches in terms of both supervised and unsupervised tasks. Next, we present a novel deep learning framework for probabilistic interpolation that significantly improves uncertainty quantification in the output interpolations. Furthermore, we show that this framework is also able to improve classification performance. As our final contribution, we study fusion architectures for learning from text data combined with irregularly sampled time series data