Iñigo Urteaga

Acquisition of human traces with Bluetooth technology: Challenges and proposals

This paper highlights the challenges to be taken into consideration when Bluetooth is used as a radio technology to capture proximity traces between people. Our study analyzes the limitations of Bluetooth-based trace acquisition initiatives carried out until now in terms of granularity and reliability. We then propose an optimal configuration for the acquisition of proximity traces and movement information using a fine-tuned Bluetooth system based on custom hardware. With this system and based on such a configuration, we have carried out an intensive human trace acquisition experiment resulting in a proximity and mobility database of more than 5million traces with a minimum granularity of 5s.

Sequential Monte Carlo methods under model uncertainty

We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive power of each model, which is updated sequentially as we observe more data. The method allows the models with better predictive powers to explore the state space with more resources than models lacking predictive power. This is done autonomously and dynamically within the SMC method. We show the validity of the presented method by evaluating it on an illustrative application.

Sequential Estimation of Hidden ARMA Processes by Particle Filtering—Part II

This is Part II of a series of two papers where we address sequential estimation of wide-sense stationary autoregressive moving average (ARMA) state processes by particle filtering. In Part I, we considered a state-space model where the state was an ARMA process of known order and where the parameters of the process could be known or unknown. In this paper, we extend our work from Part I by considering the same type of models, with the added complexity that the ARMA processes are now of unknown order. Instead of working on a scheme that first tracks the state by operating with different assumed models, and then selects the best model by using a predefined criterion, we present a method that directly estimates the state without the need of knowing the model order. We derive the transition density of the state for unknown ARMA model order, and propose a particle filter based on that density and the empirical Bayesian methodology. We demonstrate the performance of the proposed method with computer simulations and compare it with the methods from Part I.

Sequential Monte Carlo sampling for systems with fractional Gaussian processes

In the past decades, Sequential Monte Carlo (SMC) sampling has proven to be a method of choice in many applications where the dynamics of the studied system are described by nonlinear equations and/or non-Gaussian noises. In this paper, we study the application of SMC sampling to nonlinear state-space models where the state is a fractional Gaussian process. These processes are characterized by long-memory properties (i.e., long-range dependence) and are observed in many fields including physics, hydrology and econometrics. We propose an SMC method for tracking the dynamic longmemory latent states, accompanied by a model selection procedure when the Hurst parameter is unknown. We demonstrate the performance of the proposed approach on simulated time-series with nonlinear observations.

Estimation of ARMA state processes by particle filtering

There are many practical signal processing settings where a state-space model consists of a state described by an ARMA process that is observed via non-linear functions of the state. In this paper, we propose a particle filtering method for sequentially estimating the ARMA process in the presence of unknown parameters. In the considered problem, we have static and dynamic unknowns, and we show how to handle the static parameters so that the estimation of the state process does not degrade with time. We propose a new particle filter that approximates the posterior of all the unknowns by a Gaussian distribution, in combination with a Monte Carlo approach to the Rao-Blackwellization of the static parameters. We demonstrate the performance of the proposed method by extensive computer simulations.

Replication and optimization of hedge fund risk factor exposures

In this paper, we propose a novel approach for decomposing hedge fund returns onto observable risk factors. We utilize a vector stochastic-volatility model to extract the time-varying exposure of low frequency hedge fund returns on high frequency market data. We implement the estimation by using particle filtering and the concept of Rao-Blackwellization. With the latter, we remove all the static parameters of the model and thereby reduce the dimension of the parameter space for particle generation. Thus, we are able to obtain accurate estimates of the posterior distributions of the model states. For our model, this reduction is significant because the number of static parameters is large. We use the proposed model to analyze hedge fund performance and to optimally replicate hedge fund strategies economically. We demonstrate the validity and effectiveness of the method by computer simulations.

Sequential Monte Carlo sampling for correlated latent long-memory time-series

In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional Gaussian processes have interesting properties including long-memory, self-similarity and scale-invariance, and thus, are of interest for building models in finance and econometrics. We propose sequential Monte Carlo (SMC) methods for inference of the latent processes where each method is based on different assumptions about the parameters of the state-space model. The methods are extensively evaluated via simulations of the popular stochastic volatility model.

DTN Routing Optimised by Human Routines: The HURRy Protocol

This paper proposes the HURRy (HUman Routines used for Routing) protocol, which infers and benefits from the social behaviour of nodes in disruptive networking environments. HURRy incorporates the contact duration to the information retrieved from historical encounters among neighbours, so that smarter routing decisions can be made. The specification of HURRy is based on the outcomes of a thorough experiment, which highlighted the importance of distinguishing between short and long contacts and deriving mathematical relations in order to optimally prioritize the available routes to a destination. HURRy introduces a novel and more meaningful rating system to evaluate the quality of each contact and overcome the limitations of other routing approaches in social environments.

Particle filtering of ARMA processes of unknown order and parameters

This paper considers inference on the widely used state-space models described by hidden ARMA state processes of unknown order observed via non-linear functions of the states. We propose a particle filtering method for sequentially inferring the unknown ARMA time-series by Rao-Blackwellization of all the static unknowns. Our method does not rely either on any assumption on the model order or on the static ARMA and state innovation parameters. Consequently, when the ARMA model order is unknown, it can be used without a follow-up model selection procedure. Extensive simulation results validate the proposed method across different ARMA models.

Multiple particle filtering for inference in the presence of state correlation of unknown mixing parameters

We present a novel Rao-Blackwellized multiple particle filtering method for inference of correlated latent states observed via nonlinear functions. We adopt a state-space framework and model the dynamic correlated states using a mixing matrix, embedded in white Gaussian noise. The critical challenges in practice are the lack of knowledge about the mixing parameters and the possibly large dimensionality of the state. We address these issues by implementing Rao-Blackwellization of the unknown parameters and adopting a divide-and-conquer approach. The former strategy amounts to marginalizing out some of the variables; the latter breaks the space of the system in subsystems, and runs a separate particle filter for each of them. The resulting Rao-Blackwellized multiple particle filtering accurately estimates the correlated latent states, as shown by the provided simulation results.