SigCO: Mining Significant Correlations via a Distributed Real-time Computation Engine

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When activist shareholders file Schedule 13D filings, the average excess return on target stocks is 6% and stock price volatility drops by about 10%. Prior to filing days, volatility (price) information is reflected in option (stock) prices. Using a compre ...

In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitor ...

Process industry firms have thrived in recent decades, but changes in the markets are currently putting both growth and profitability at risk. In this context, inventory management is increasingly viewed as an essential lever for creating a sustainable com ...

This thesis presents new flexible dynamic stochastic models for the evolution of market prices and new methods for the valuation of derivatives. These models and methods build on the recently characterized class of polynomial jump-diffusion processes for w ...

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no excep ...

Elliptical distributions are useful for modelling multivariate data, multivariate normal and Student t distributions being two special classes. In this paper, we provide a definition for the elliptical tempered stable (ETS) distribution based on its charac ...

Location-aware data collection technologies provide new insights about location choices. Only a few dynamic models of location choice exist in scientific literature. To our knowledge, none of them correct for serial correlation. In this paper, we model cho ...

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

As various kinds of sensors penetrate our daily life (e.g., sensor networks for environmental monitoring, GPS for localization and navigation), the efficient management of massive amount of sensor data becomes increasingly important at present. Many sensor ...

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

In recent years we are experiencing a dramatic increase in the amount of available time-series data. Primary sources of time-series data are sensor networks, medical monitoring, financial applications, news feeds and social networking applications. Availab ...

We introduce a hybrid discrete choice framework to model the decisions of investors in stock markets. More specifically, we model the decision to buy or sell stocks using a binary logit model with latent classes, characterizing the perception of risk. The ...

This dissertation describes a complete methodological framework for designing, modeling and optimizing a specific class of distributed systems whose dynamics result from the multiple, stochastic interactions of their constitutive components. These componen ...

This thesis tackles new challenges associated with the disaggregate modeling of the human behavior. Decision-aid tools help in making decisions, by providing quantitative insights on the decisions and associated consequences. They are useful in complex sit ...

Coupled neural networks, the Internet, World Wide Web, social networks and interacting biological networks are few examples of systems which consist of a large number of interacting dynamical units. Collective behavior of such systems is a consequence of t ...

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables lik ...

In standard database scenarios, an end-user assumes that all data (e.g., sensor readings) is stored in a database. Therefore, one can simply submit any arbitrary complex processing in the form of SQL queries or stored procedures to a database server. Data ...

This dissertation consists of three chapters. The first chapter empirically investigates how the intensity of product market competition affects the cost of debt. Using a large sample of loans to publicly traded US manufacturing firms, the chapter provides ...

This thesis consists of three chapters. The first chapter endogenizes technological change by introducing a stylized innovation process driven by a R&D–dependent Poisson process in a Cox, Ingersoll and Ross (1985) production economy. The model reproduces s ...

The radiant external environment may be described by two hemispheres, above and below the horizontal plane, which are discretised into patches of known solid angle. Occlusions to these patches may be combined and represented as some patch fraction for whic ...

SigCO: Mining Significant Correlations via a Distributed Real-time Computation Engine

Chattez avec Graph Search

Informed Trading in the Stock Market and Option Price Discovery

Distributed Time Series Analytics

Inventory dynamics in process industries: An empirical investigation

Polynomial models in finance

Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop

Elliptical tempered stable distribution

Location choice with longitudinal WiFi data

RUBIK: Efficient Threshold Queries on Massive Time Series

Online indexing and distributed querying model-view sensor data in the cloud

DiNoDB: Efficient Large-Scale Raw Data Analytics

Statistical Models for Querying and Managing Time-Series Data

Modeling the behavior of investors

Design, Modeling and Optimization of Stochastic Reactive Distributed Robotic Systems

New Challenges in Disaggregate Behavioral Modeling : Emotions, Investments and Mobility

Distributed Dynamics on Networks and their Applications in Network Communities, Sensor Networks and EEG's

Robust Multivariate and Nonlinear Time Series Models

Design and implementation of an efficient data stream processing system

Corporate Finance, Asset Returns, and Credit Risk

Essays on Equilibrium Asset Pricing

Holistic Radiation Modelling with a Fast Simplified Radiosity Algorithm