Forecasting human entrances at a commercial store using facial recognition data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Optimização do processo de recolha de resíduos: desenvolvimento de ferramentas de investigação operacional para o problema de orientação de equipas com multi-restrições

Tese de Doutoramento em Engenharia Industrial e de Sistemas.

Desarrollo de metodologias de propagación y cultivo de plantas nativas de Córdoba con potencialornamental. Development of propagation and cultivation methodologies of native plants of Córdoba withornamental potential.

El problema: La flora nativa de Córdoba, actualmente amenazada, es rica en especies con potencial ornamental que todavía no se cultivan. El uso de estas plantas está limitado por la escasez de material biológico, la falta de conocimientos de su propagación y su insuficiente valoración pública. Hipótesis: La propagación y el cultivo de una amplia gama de plantas nativas cordobesas con potencial ornamental son técnicamente factibles y tienen potencial productivo y económico. Objetivo: Promover el uso de especies nativas ornamentales en la provincia de Córdoba. Objetivos específicos: 1. Desarrollar metodologías de propagación y cultivo de especies nativas con potencial ornamental; 2. Transferir estas metodologías a viveros privados y públicos; 3. Difundir los conocimientos obtenidos a instituciones educativas. Métodos: 1- Colección: Se realizarán viajes de campo para obtener semillas o esquejes de al menos 6 especies nativas seleccionadas. Las semillas se limpiarán y se conservarán en frío. 2- Propagación: En la primavera se sembrarán 100 semillas por especie, accesión y tratamiento; se registrará porcentaje y tiempo de germinación. Los plantines se trasplantarán a almácigos, se registrará supervivencia y crecimiento. Para la propagación vegetativa, se trasplantarán esquejes de estolones directamente a macetas. 3- Trasplante: En verano, los plantines se trasplantarán a macetas grandes; se registrará supervivencia y crecimiento durante un año. 4- Documentación: Se elaborarán protocolos de las metodologías adecuadas para la propagación de las especies, usos ornamentales y características relevantes. 5- Transferencia: Los protocolos, muestras de semillas y de plantas, se transferirán a dos viveros que se comprometan a continuar con el cultivo de las especies. 6- Difusión: Se realizarán cursos, talleres, charlas y pasantías para dar a conocer la propagación de plantas nativas en instituciones educativas, desde la primaria hasta la universidad. Resultados y productos esperados: 1- Protocolos de propagación de al menos 6 especies nativas ornamentales y su transferencia a viveros, como base de una actividad productiva novedosa. 2- Un aporte a la conservación ex situ y el uso sostenible de la flora nativa. 3- Una mayor valoración de esta en la comunidad educativa. Importancia: 1- Desarrollo de la producción de plantas nativas con valor ornamental, como una alternativa económica. 2- Conservación ex situ y uso sostenible de la flora nativa. 3- Difusión del conocimiento del valor ornamental de las nativas. 4. Información científica de la biología y ecología de especies nativas. Pertinencia: Productos (ver Resultados). El impacto inmediato esperado es un aumento en la propagación, la producción, la demanda, la comercialización y el uso de plantas ornamentales nativas. Se espera también la generación de nuevos conocimientos y el estímulo de líneas de investigación biológicas y agronómicas.

Long-term Forecasting of Frame Alignment Losses for Circuit Emulation Implementation

The article provides a method for long-term forecast of frame alignment losses based on the bit-error rate monitoring for structure-agnostic circuit emulation service over Ethernet in a mobile backhaul network. The developed method with corresponding algorithm allows to detect instants of probable frame alignment losses in a long term perspective in order to give engineering personnel extra time to take some measures aimed at losses prevention. Moreover, long-term forecast of frame alignment losses allows to make a decision about the volume of TDM data encapsulated into a circuit emulation frame in order to increase utilization of the emulated circuit. The developed long-term forecast method formalized with the corresponding algorithm is recognized as cognitive and can act as a part of network predictive monitoring system.

Forecasting volatility using a continuous time model

This paper evaluates the forecasting performance of a continuous stochastic volatility model with two factors of volatility (SV2F) and compares it to those of GARCH and ARFIMA models. The empirical results show that the volatility forecasting ability of the SV2F model is better than that of the GARCH and ARFIMA models, especially when volatility seems to change pattern. We use ex-post volatility as a proxy of the realized volatility obtained from intraday data and the forecasts from the SV2F are calculated using the reprojection technique proposed by Gallant and Tauchen (1998).

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting the European Carbon Market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.