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A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance offers the first systematic application of these methods to specialized financial problems: option pricing, credit risk modeling, volatility estimation and more. The book provides tools for sorting through turbulence, volatility, emotion, chaotic events - the random 'noise' of financial markets - to analyze core components.

In recent times, a phenomenon that threatens the representative democracy of many developed countries is the low voter turnout. Voting Advice Applications (VAAs) are used to inform citizens about the political stances of the parties that involved in the upcoming elections, in an effort to facilitate their decision making process and increase their participation in this democratic process. VAA is a Web application that calls the users and parties to state their position in a set of policy statements, which are based on the current affairs of their country and then it recommends to each user the party that better fits their political views. SVAAs, a social recommendation approach of VAAs, on the other hand, base their recommendation on the VAA community of users in a collaborative filtering manner. In this paper we resort to Hidden Markov Models (HMMs) in an attempt to improve the effectiveness of SVAAs. In particular, we try to model party-supporters using HMMs and then use these models to recommend each VAA user the party whose model best fits his/her answer sequence of the VAA policy statements. HMMs proved to be effective machine learning tools for sequential and correlated data and this is the main rationale behind this study. VAA policy statements are usually correlated and grouped into categories such as external policy, economy, society, etc. As a result, opting from the various answer choices in each policy statement might be related with selections in previous and subsequent policy statements. Given that the order of policy statements is kept fixed within each VAA one can assume that (a) answer patterns (sequences of choices for all policy statements included in the VAA) can be found that characterise 'typical' voters of particular parties, and (b) the answer choice in each policy statement can be 'predicted' from previous answer choices. For our experiments we use three datasets based on the 2014 elections to the European Parliament (http://www.euvox2014.eu/), which are publicly available through the Preference Matcher website (http://www.preferencematcher.org/?page_id=18).

Web services are a technology that has been growing rapidly since its inception earlier this century. They are so popular nowadays that they are being used for numerous purposes, spanning business, healthcare, education and government applications. They are used for accessing diverse types of data in domains such as the financial, climate forecasting and sports, among others. As a result of this increased popularity, it is very common to find a large number of applications providing exactly the same service. Having a large number of applications to choose from, one that integrates a selection mechanism using quality criteria for selecting the best choice, has an advantage over other applications that do not provide this feature. However, the selection of the best Web service, among various services with the same functionality, is not an easy task. Therefore, the design of a worthwhile technique to determine the best Web service by assessing its quality of service is important. Hidden Markov models are probabilistic methods that allow us to build behavior models of the Web services. Such models can be used to predict their behavior in the near future. In this paper, we propose a method for selecting Web services based on the response time QoS parameter and hidden Markov models.

AbstractAn essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime‐ and terrorism‐related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model‐based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.

Context Direct observations of animals are the most reliable way to define their behavioural characteristics; however, to obtain these observations is costly and often logistically challenging. GPS tracking allows finer-scale interpretation of animal responses by measuring movement patterns; however, the true behaviour of the animal during the period of observation is seldom known. Aims The aim of our research was to draw behavioural inferences for a lioness with a hidden Markov model and to validate the predicted latent-state sequence with field observations of the lion pride. Methods We used hidden Markov models to model the movement of a lioness in the Kruger National Park, South Africa. A three-state log-normal model was selected as the most suitable model. The model outputs are related to collected data by using an observational model, such as, for example, a distribution for the average movement rate and/or direction of movement that depends on the underlying model states that are taken to represent behavioural states of the animal. These inferred behavioural states are validated against direct observation of the pride's behaviour. Key results Average movement rate provided a useful alternative for the application of hidden Markov models to irregularly spaced GPS locations. The movement model predicted resting as the dominant activity throughout the day, with a peak in the afternoon. The local-movement state occurred consistently throughout the day, with a decreased proportion during the afternoon, when more resting takes place, and an increase towards the early evening. The relocating state had three peaks, namely, during mid-morning, early evening and about midnight. Because of the differences in timing of the direct observations and the GPS locations, we had to compare point observations of the true behaviour with an interval prediction of the modelled behavioural state. In 75% of the cases, the model-predicted behaviour and the field-observed behaviour overlapped. Conclusions Our data suggest that the hidden Markov modelling approach is successful at predicting a realistic behaviour of lions on the basis of the GPS location coordinates and the average movement rate between locations. The present study provided a unique opportunity to uncover the hidden states and compare the true behaviour with the inferred behaviour from the predicted state sequence. Implications Our results illustrated the potential of using hidden Markov models with movement rate as an input to understand carnivore behavioural patterns that could inform conservation management practices.