Catherine Combes

Generalized Extreme Value distribution for fitting opening/closing asset prices and returns in stock- exchange

Robust estimation of stock-exchange fluctuations is a challenging problem. The accuracy of statistical extrapolation is fairly sensitive to both model and sampling error. Using the opening/closing quotation and return data (concerning stock-exchange), this paper presents a comparative assessment using various theoretical distributions: Normal, LogNormal, Gamma, Gumbel, Weibull, Generalized Extreme Value (GEV).We used GEV distribution in an other context than extreme value theory (indeed dedicated to this domain). From the empirical distribution on short periods (3, 6, 9 and 12 months), we prove that GEV distribution allows to correctly fit returns and opening/closing quotations (without studying only the behaviour of maxima or minima in a sample, but overall of the sample) by comparison with the other distributions. This paper focuses on the GEV distribution in the univariate case. Following a review of the literature, univariate GEV distribution is applied to a series of daily stock-exchange of TOTAL oil company. We illustrate this article with the opening/closing quotations minus the moving average of the five last days and the returns of this company on short and medium terms (3, 6, 9, 12 months moving forward 1 month).

Mathematical Models Applied to On-Chip Network on FPGA for Resource Estimation

One main challenge of prototyping a SoC (System on Chip) on FPGA (Field Programmable Gate Array) is to tune at best the communication architecture according to the task graph of an application and the available resources of the chosen FPGA. The exploration of the potential design candidates is time consuming, tedious and does not scale. The sheer number of parameters leads to a wide design space that cannot be explored in a limited time. The aim of this paper is to identify mathematical models applied to NoC to estimate FPGA resources. Mathematical models are obtained from a database containing a set of observed results. Using the database, the Pearsons correlation coefficient and the variable clustering are used to set the most appropriate variables and constants. The mathematical models are obtained and then validated with a set of experimental results. The validation shows that the error rate between observed results and the analytically estimated results is less than 5%. The designer can therefore tune the NoC in shorter exploration time.

Towards decision support for a home care services platform

It is believed that ICT-mediation for home care services increases patient empowerment, independency, self-efficacy and quality of life. Providing elderly people with tailored care services allows us to learn from patient data to predict future care needs. In this article, we demonstrate the contribution of machine learning to homecare services, using data collected by a home care services platform. As an actual case, we show how simulated medication compliance can be measured and modeled using clustering and regression techniques. The approach is validated using data from French nursing home databases. The results show that it is possible to classify situations in elderly healthcare, and schedule resource planning according to expected health problems.

Mathematical Modeling for NoC resources Estimation on FPGA

NoCs ( Network - on - Chip ) have emerged as efficient scalable and low power communication structures for SoC (System - On - Chip). Two main challenges are pointed out when prototyping a SoC on a reconfigurable chip such as FPGA ( Field P rogrammable Gate A rray). The first challenge is to tune a NoC according to the application requirements by exploring all design solutions . The second challenge is to dimension the FPGA resources regarding the previously s elected appropriate solution. Usually, dimensioning of FPGA resources is done by several runs of automatic synthesis processes to evaluate if the number of resources fit s to the selected FPGA device. Finding the most appropriate solution and FP GA dimensioning are time consuming and the design space exploration is not fully done in order to decrease the exploration tim e. A more appropriate solution would be analytic model s of the NoC on a FPGA device . Mathematical modelling consists in identifyin g links between the NoC parameters and the FPGA resources using a database and in extracting relations between them. In this paper, we present a methodological framework to estimate the number of resources required for a given communication architecture a nd the task graph of the application . The framework contains 4 steps : the design or the selection of the NoC, the data collection, the data analysis from which a model is deduced . The database obtained in the data collection step contains the synthesized r esults of each NoC configuration. Two NoCs , with a mesh topology and different characteristics, are used to provide two databases. The methodological framework provides t he most appropriate models that are identified using predictive modelling . The evaluation of each model shows that the relative error is less than 5% in most cases. It is therefore possible to tune the most appropriate NoC and to estimate the required resources in a short exploration time without the synthesis steps

Inference of Markov Chain Models by Using k-Testable Language: Application on Aging People

We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). Grammatical Inference (also known as automata induction, grammar induction and automatic language acquisition) allows grammar and language learning from data. Machine learning by using grammar has a variety of applications: pattern recognition, adaptive intelligent agents, diagnosis, biology, systems modelling, prediction, natural language acquisition, data mining… The proposed approach is based on regular grammar. An adaptation of k-Testable Languages in the Strict Sense Inference algorithm is proposed in order to infer a probabilistic automaton from which a Markovian model which has a discrete (finite or countable) state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.

A metamodel to manage OLAP information

Nowadays, online analytical processing (OLAP) information of decisional systems is defined in text. Consequently, this information is difficult to manage. However, OLAP elements are shared by all decisional systems: they can so be modelled. We propose in this article a metamodel to define OLAP information. The metamodel is written in unified modelling language (UML). The aim of this metamodel is to provide a unique reference for all persons in charge of OLAP information management and definition; it can be used for designing, construction, development and maintenance purpose.