We have developed metrics of the performance and operational impacts of METOC support to strike operations conducted on operational aircraft carriers (CVs). Our goal was to assess that support and make recommendations on improving it. We adapted an existing automated real time METOC metrics system, which was developed for land based training missions, for use on CVs by developing a new data collection form, new metrics, and new collection, analysis, and reporting architecture for the remote entering of sensitive mission data without compromise. The weather support element of a CV, the OA division, does not provide strike mission planning support, but does provide situational awareness to pilots. Our system allows that situational awareness to be measured and assessed using metrics that quantify the performance of the forecasts, the relationship of the forecasts to the mitigating actions taken by pilots due to adverse weather conditions, and the effects of individual weather phenomena on the execution of strike missions. A key element of the data collection, analysis, and reporting system developed in this study is the collection of METOC related data from pilots during their intelligence debriefings. This system is readily adaptable for the assessment of METOC support to other warfare areas.

A Markov model was used to determine the optimal number of Medical Service Corps Health Care Administrator officers the Navy must access each year in order to maintain a desired end-strength. The Markov model identified the flow of Ensigns through Lieutenant Commanders using sixteen-year data. Five scenarios were analyzed to determine the most qualified method in determining accession levels. Optimization was achieved by changing with distribution of accessions sources and recruiting ranks. The solver scenario provided the alternative with the least amount of underage and overage when comparing the force structure to the predicted values. A four year historical review identified that if the current business practices will not allow for rank steady states to be reached. A few significant characteristics were determined to influence retention at seven and ten year periods. The characteristic of primary concern, commissioning source, was determined to be significant. A survival analysis identified that the In-Service Procurement Program has a different survival function than other sources. Increased variations between the current force structure plan and the predicted Markov model outcomes suggest that greater efficiency could be obtained in future years. This Markov model can be used as a tool for accessioning to improving extended forecasts.

Accurate weather forecasts are vital to air combat operations. Quantitative assessments of forecasts and their operational impacts are essential to improving weather support for war fighters. We adapted an existing U.S. Navy, web-based, near-real time system for collecting and analyzing data on the performance and operational impacts of military forecasts. We used the adapted system to collect and analyze data on Air Force Weather (AFW) forecasts, and the planning and execution of flying operations, at six Air Combat Command and Pacific Air Forces bases. We analyzed the data to develop quantitative metrics of forecast performance and operational impacts. Our results indicate that planning weather forecasts (PWFs) have a higher potential for making positive contributions to air operations than do mission execution forecasts (MEFs). This is notable because AFW units spend significantly less time developing PWFs than MEFs. Surface visibility, cloud ceilings, and cloud layers caused most negative mission impacts, indicating these phenomena should be a focus of future research and training. We found high levels of mission success even when forecasts were inaccurate, perhaps due to aircrew and mission flexibility. Our analyses revealed a need for improved education of flying units on the nature and availability of AFW products.

The area of order picking has been widely studied but still there is no general approach covering all the potential variables of a unique case. Optimizing the product placement is a useful way to improve order picking through reduced travel distances and goes hand in hand with an appropriate batching strategy and routing policy. The existing methods are of mathematical basis and can be costly to implement and it is therefore necessary to develop a non-mathematical approach that can be used for distribution centers and in this particular case, the warehouse of a mail order company. Literature is reviewed to investigate the current situation of a large Swedish mail order retailer for the development of a unique product assignment strategy, taking into consideration potential variables. It was found out that a lot of trade-offs are being done all the time between travel distances of the SKUs. Also, it was found out that “cart blocking” might be a potential problem when using a turnover-based storage assignment strategy.

Demand participation is a basic ingredient of the next generation of power exchanges in electricity markets. A key challenge in implementing demand response stems from establishing reliable market frameworks so that purchasers can estimate the demand correctly, buy as economically as possible and have the means of hedging the risk of lack of supply. System operators also need ways of estimating responsive load behaviour to reliably operate the grid. In this context, two aspects of demand response are addressed in this study: scheduling and baseline estimation. The thesis presents a market clearing algorithm including demand side reserves in a two-stage stochastic optimization framework to account for wind power production uncertainty. The results confirm that enabling the load to provide reserve can potentially benefit consumers by reducing electricity price, while facilitating a higher share of renewable energy sources in the power system. Two novel methods, Bayesian Linear regression and Kernel adaptive filtering, are proposed for baseline load forecasting in the second part of the study. The former method provides an integrated solution for prediction with full accounting for uncertainty while the latter provides an online sequential learning algorithm that is useful for short term forecasting. / Graduate / 0544 / nimahtehrani@gmail.com

Whether we would like to model imports and exports, or forecast inflation, structural variation in an economy frequently causes problems. This thesis examines such variation in two dimensions: first, in a cross-section of individuals, and secondly, over time. A panel of manufacturing industries in several developed countries reveals that there is substantial variation across sectors, in the response of trade to changes in prices and incomes. Ignoring this heterogeneity can render conventional results biased and inconsistent, so a number of robust methods are used to obtain reliable estimates of long-run and short-run trade relationships. The findings point to common behaviour across sectors, which could be due to similarities in technology. The impact of structural breaks over time is examined in the second part of the thesis. Unpredictable shifts in deterministic terms such as the mean of a process are shown to generate significant forecast failure, and even the methods used to evaluated forecast accuracy are affected. Using the Kullback-Leibler discrepancy to measure the size of forecast errors, various robust mechanisms are discussed, that do not fail systematically after a break. Although they can provide a degree of insurance if a shift does occur, this comes at a cost if there is no change, and in the presence of measurement error they can exacerbate the uncertainty surrounding a forecast. An empirical illustration with a model of UK money demand provides some support for the automatic correction mechanisms, although there does seem to be a role for direct modeling of a break process.

We provide a comprehensive analysis of the out-of-sample predictive accuracy of different global vector autoregressive (GVAR) specifications based on alternative weighting schemes to address global spillovers across countries. In addition to weights based on bilateral trade, we entertain schemes based on different financial variables and geodesic distance. Our results indicate that models based on trade weights, which are standard in the literature, are systematically outperformed in terms of predictive accuracy by other specifications. We find that, while information on financial linkages helps improve the forecasting accuracy of GVAR models, averaging predictions by means of simple predictive likelihood weighting does not appear to systematically lead to lower forecast errors.

In June 2008 one of the biggest and most popular sports tournaments took place in Austria and Switzerland, the European football championship 2008 (UEFA EURO 2008). Before the tournament started millions of football supporters throughout the world were asking themselves, just as we did: "Who is going to win the EURO 2008?". We investigate a method for forecasting the tournament outcome, that is not based on historical data (such as scores in previous matches) but on quoted winning odds for each of the 16 teams as provided by 45 international bookmakers. By using a mixed-effects model with a team-specific random effect and fixed effects for the bookmaker and the preliminary group we model the unknown "true" log-odds for winning the championship. The final of the EURO 2008 was played by the teams Germany and Spain. This was exactly the fixture that our method forecasted with a probability of about 20.2%. Furthermore, estimated winning probabilities can be derived from our model, where team Germany, the runner-up of the final had the highest probability (17.6%) to win the title and team Spain the winner of the tournament had the second best chance to win the championship (12.3%). To adjust for effects of the tournament schedule including the group draw, we recovered the latent team strength (underlying the bookmakers' expectations) to answer the question: Will the "best" team win? An ex post analysis of the tournament showed that our method yields good predictions of the tournament outcome and outperforms the FIFA/Coca Cola World rating and the Elo rating. / Series: Research Report Series / Department of Statistics and Mathematics

United States intelligence officers and policymakers need reliable forecasts of country, regional, and global stability or instability. Such forecasts require a methodology for identifying and analyzing factors that contribute to stability. The anticipation of this stability level can facilitate crisis warning and diplomatic strategies for various timelines, including five, ten, and twenty year forecasts. While the problem of forecasting can be tackled in various ways, in the interest of time and space, I will only go into a few of them. The approach I will use is multiple linear regression to generate a short-term forecast for the stability levels of the countries of the Former Soviet Union (FSU). This model could ultimately be used to help formulate policies that enhance stability in developing or transitioning countries.

The exchange rate forecasting has been an interesting topic for a long time. Beating the random walk model has been the goal of many researchers, who applied various techniques and used various datasets. We tried to beat it using bayesian model averaging technique, which pools a large amount of models and the final forecast is the average of forecasts of these models. We used quarterly data from 1980 to 2013 and attempted to predict the value of exchange rate return of five currency pairs. The novelty was the fact that none of these currency pairs included U.S. Dollar. The forecasting horizon was one, two, four and eight quarters. In addition to random walk, we also compared our results to historical average return model using several benchmarks, such as root mean squared error, mean absolute error or direction of change statistic. We found out that bayesian model averaging can not generally outperform random walk or historical average return, but in specific setting it can produce forecasts with low error and with high percentage of correctly predicted signs of change.