Evaluation of Weather Forecasting Services for the Iowa Department of Transportation, CTRE Project 02-126, 2003

The objective of the evaluation of the weather forecasting services used by the Iowa Department of Transportation is to ascertain the accuracy of the forecasts given to maintenance personnel and to determine whether the forecasts are useful in the decision-making process and whether the forecasts have potential for improving the level of service. The Iowa Department of Transportation has estimated the average cost of fighting a winter storm to be about $60,000 to $70,000 per hour. This final report is to provide an evaluation report describing the collection of weather data and information associated with the weather forecasting services provided to the Iowa Department of Transportation and its maintenance activities and to determine their impact in winter maintenance decision-making.

Application of WRF-Chem to Forecasting PM10 Concentration Over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one-way nested domains using the GFS meteorological data and the TNO MACC II emissions. The 48 hour forecasts were run for each day of the winter and summer period of 2014 and there is only a small decrease in model performance for winter with respect to forecast lead time. The model in general captures the variability in observed PM10 concentrations for most of the stations. However, for some locations and specific episodes, the model performance is poor and the results cannot yet be used by official authorities. We argue that a higher resolution sector-based emission data will be helpful for this analysis in connection with a focus on planetary boundary layer processes in WRF-Chem and their impact on the initial distribution of emissions on both time and space.

Sovrastrutture non pavimentate rinforzate: verifica con metodi numerici.

Nella presente tesi si studia lo stato di deformazione di una strada non pavimentata, rinforzata con geotessile, indotto dal passaggio di carrelli semoventi SMPT. L’obiettivo è verificare che le deformazioni siano compatibili con i risultati provenienti dai metodi di calcolo analitici. Durante lo sviluppo ci si avvale di modelli 3-dimensionali del terreno creati ex-novo e di simulazioni alle differenze finite per quanto riguarda l’interazione tra carichi e sovrastruttura. Tale simulazione è stata condotta con il software FLAC3D. Si è simulato dapprima il comportamento della sovrastruttura non rinforzata sollecitata da: singolo asse e carrello a 4 assi. In secondo luogo, si è analizzato il modello del terreno rinforzato, nel quale si introduce una geogriglia a diverse profondità per valutare quella ottimale. Sono stati creati così tre modelli distinti nei quali il rinforzo è posizionato ad 1/3 dell’altezza di aggregato, a 2/3 ed all’interfaccia tra aggregato e primo strato del terreno naturale. Il risultato mostra che il posizionamento ottimale della geogriglia non è all’interfaccia come espresso dalle teorie in merito, ma a 2/3 dell’altezza dello strato di aggregato, dove viene permessa una maggiore deformazione ma un minor stato di sollecitazione del materiale di rinforzo. Infine si valuta in maniera marginale il caso del terreno rinforzato, dove l’altezza dello strato di aggregato viene calcolato con i metodi analitici di Giroud e Noiray (1980) e Giroud e Han (2004), dei quali viene in principio fornita una rigorosa esposizione teorica. In questi ultimi casi, i risultati non sono soddisfacenti. Infatti si è trovato che il raggiungimento del carico di rottura della geogriglia limita fortemente le deformazioni, rendendole così insufficienti per mobilitare le pressioni di contatto richieste. Concludendo si gettano le basi per futuri elaborati, consigliando alcune modifiche da apportare per perfezionare la modellistica dei casi rinforzati.

Schedulazione delle rotte marittime nel settore ortofrutticolo. Modelli e Metodi

Il trasporto marittimo è una delle modalità più utilizzate soprattutto per la movimentazione di grandi volumi di prodotti tra i continenti in quanto è a basso costo, sicuro e meno inquinante rispetto ad altri mezzi di movimentazione. Ai giorni nostri è responsabile di circa l’80% del commercio globale (in volume di carichi trasportati). Il settore del trasporto marittimo ha avuto una lunga tradizione di pianificazione manuale effettuata da progettisti esperti.
 L’obiettivo principale di questa trattazione è stato quello di implementare un modello matematico lineare (MILP, Mixed-Integer Linear Programming Model) per l’ottimizzazione delle rotte marittime nell’ambito del mercato orto-frutticolo che si sviluppa nel bacino del Mediterraneo (problema di Ship-Scheduling). Il modello fornito in questa trattazione è un valido strumento di supporto alle decisioni che può utilizzare uno spedizioniere nell’ambito della pianificazione delle rotte marittime della flotta di navi in suo possesso. Consente di determinare l’insieme delle rotte ottimali che devono essere svolte da un insieme di vettori al fine di massimizzare il profitto complessivo dello spedizioniere, generato nell’arco di tempo considerato. Inoltre, permette di ottenere, per ogni nave considerata, la ripartizione ottimale della merce (carico ottimale).

Group Forecasting Accuracy in Hotels

Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10-15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.

Analisi dei principi e dei metodi per la valutazione della sostenibilità dei prodotti e dei processi con un'applicazione ai componenti per l’edilizia

Tale elaborato si pone l’obiettivo di analizzare una tematica oggigiorno molto discussa, ma tuttora per molti versi inesplorata: la sostenibilità. Esso è stato scritto con la volontà di rendere disponibile uno scritto di consultazione che fornisca una panoramica il più possibile completa sugli studi e le metodologie applicative elaborati fino ad ora connessi al tema della sostenibilità. La logica con cui lo scritto è articolato, prevede in primis un inquadramento generale sul tema della sostenibilità, fortemente connesso con il concetto di Life Cycle Thinking, e prosegue concentrando l’attenzione su aspetti via via più specifici. Il focus dell’analisi si concentra infatti sullo studio delle singole tecniche del ciclo di vita e successivamente sulle potenzialità di applicazione delle stesse ad uno specifico settore: quello edilizio. All’interno di questo settore è poi fornito un dettaglio in merito ai materiali ceramici per i quali si è intrapreso un serio percorso verso l’applicazione concreta dei principi dello sviluppo sostenibile. Per consolidare i temi trattati, l’elaborato si concentra infine sull’analisi di due studi applicativi: uno studio di Life Cycle Assessment e uno di Life Cycle Costing realizzati al fine di studiare i profili ambientale ed economico delle piastrelle ceramiche in contrapposizione a quelle in marmo.

Metodi e strumenti di Facilty Management: una proposta di integrazione tra Piano di Manutenzione e Fascicolo dell’Opera nel caso della Grande Distribuzione Organizzata

Gli ultimi 10 anni hanno visto un crescente aumento delle richieste di fornitura di servizi legati alla manutenzione edilizia da parte della Grande Distribuzione Organizzata; la domanda è quella di servizi riconducibili al Facility Management, ovvero rapporti basati sul raggiungimento di standard qualitativi predefiniti in sede contrattuale e garanzia di intervento 24h/24. Nella prima parte del progetto di tesi viene inquadrata la disciplina del FM, le motivazioni, gli strumenti e gli attori coinvolti. Dopo un excursus normativo sulla manutenzione in Italia, una classificazione delle tipologie di intervento manutentivo e una valutazione sull’incidenza della manutenzione nel Life Cycle Cost, viene effettuata un’analisi delle modalità interoperative del FM applicato alla manutenzione edilizia nel caso della GDO. La tesi è stata svolta nell'ambito di un tirocinio in azienda, il che ha permesso alla laureanda di affrontare il caso di studio di un contratto di Global Service con un’importante catena di grande distribuzione, e di utilizzare un software gestionale (PlaNet) con il quale viene tenuta traccia, per ogni punto vendita, degli interventi manutentivi e della loro localizzazione nell’edificio. Questo permette di avere un quadro completo degli interventi, con modalità di attuazione già note, e garantisce una gestione più efficace delle chiamate, seguite tramite un modulo di Call Center integrato. La tesi esamina criticamente i principali documenti di riferimento per l’opera collegati alla manutenzione: il Piano di Manutenzione e il Fascicolo dell’Opera, evidenziando i limiti legati alla non completezza delle informazioni fornite. L’obbiettivo finale della tesi è quello di proporre un documento integrativo tra il Piano di Manutenzione e il Fascicolo, al fine di snellire il flusso informativo e creare un documento di riferimento completo ed esaustivo, che integra sia gli aspetti tecnici delle modalità manutentive, sia le prescrizioni sulla sicurezza.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

Capacity forecasting for radio access networks

The mobile networks market (focus of this work) strategy is based on the consolidation of the installed structure and the optimization of the already existent resources. The increasingly competition and aggression of this market requires, to the mobile operators, a continuous maintenance and update of the networks in order to obtain the minimum number of fails and provide the best experience for its subscribers. In this context, this dissertation presents a study aiming to assist the mobile operators improving future network modifications. In overview, this dissertation compares several forecasting methods (mostly based on time series analysis) capable of support mobile operators with their network planning. Moreover, it presents several network indicators about the more common bottlenecks.

Do U.S. and Colombian macro factors improve the forecasting ability of unrestricted VAR models of the local term structure of interest rate?

Este estudio empírico compara la capacidad de los modelos Vectores auto-regresivos (VAR) sin restricciones para predecir la estructura temporal de las tasas de interés en Colombia -- Se comparan modelos VAR simples con modelos VAR aumentados con factores macroeconómicos y financieros colombianos y estadounidenses -- Encontramos que la inclusión de la información de los precios del petróleo, el riesgo de crédito de Colombia y un indicador internacional de la aversión al riesgo mejora la capacidad de predicción fuera de la muestra de los modelos VAR sin restricciones para vencimientos de corto plazo con frecuencia mensual -- Para vencimientos de mediano y largo plazo los modelos sin variables macroeconómicas presentan mejores pronósticos sugiriendo que las curvas de rendimiento de mediano y largo plazo ya incluyen toda la información significativa para pronosticarlos -- Este hallazgo tiene implicaciones importantes para los administradores de portafolios, participantes del mercado y responsables de las políticas

Live and Feeder Cattle Options Markets: Returns, Risk, and Volatility Forecasting

unpublished

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.

Forecasting exchange rates in the presence of instabilities

Many exchange rate papers articulate the view that instabilities constitute a major impediment to exchange rate predictability. In this thesis we implement Bayesian and other techniques to account for such instabilities, and examine some of the main obstacles to exchange rate models' predictive ability. We first consider in Chapter 2 a time-varying parameter model in which fluctuations in exchange rates are related to short-term nominal interest rates ensuing from monetary policy rules, such as Taylor rules. Unlike the existing exchange rate studies, the parameters of our Taylor rules are allowed to change over time, in light of the widespread evidence of shifts in fundamentals - for example in the aftermath of the Global Financial Crisis. Focusing on quarterly data frequency from the crisis, we detect forecast improvements upon a random walk (RW) benchmark for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the time-varying parameters of the Taylor rules to differ between countries. In Chapter 3 we look closely at the role of time-variation in parameters and other sources of uncertainty in hindering exchange rate models' predictive power. We apply a Bayesian setup that incorporates the notion that the relevant set of exchange rate determinants and their corresponding coefficients, change over time. Using statistical and economic measures of performance, we first find that predictive models which allow for sudden, rather than smooth, changes in the coefficients yield significant forecast improvements and economic gains at horizons beyond 1-month. At shorter horizons, however, our methods fail to forecast better than the RW. And we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients variability to incorporate in the models, as the main factors obstructing predictive ability. Chapter 4 focus on the problem of the time-varying predictive ability of economic fundamentals for exchange rates. It uses bootstrap-based methods to uncover the time-specific conditioning information for predicting fluctuations in exchange rates. Employing several metrics for statistical and economic evaluation of forecasting performance, we find that our approach based on pre-selecting and validating fundamentals across bootstrap replications generates more accurate forecasts than the RW. The approach, known as bumping, robustly reveals parsimonious models with out-of-sample predictive power at 1-month horizon; and outperforms alternative methods, including Bayesian, bagging, and standard forecast combinations. Chapter 5 exploits the predictive content of daily commodity prices for monthly commodity-currency exchange rates. It builds on the idea that the effect of daily commodity price fluctuations on commodity currencies is short-lived, and therefore harder to pin down at low frequencies. Using MIxed DAta Sampling (MIDAS) models, and Bayesian estimation methods to account for time-variation in predictive ability, the chapter demonstrates the usefulness of suitably exploiting such short-lived effects in improving exchange rate forecasts. It further shows that the usual low-frequency predictors, such as money supplies and interest rates differentials, typically receive little support from the data at monthly frequency, whereas MIDAS models featuring daily commodity prices are highly likely. The chapter also introduces the random walk Metropolis-Hastings technique as a new tool to estimate MIDAS regressions.

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

A review on the recent history of wind power ramp forecasting.

Forecasting large and fast variations of wind power (the so called ramps) helps achieve the integration of large amounts of wind energy. This paper presents a survey on wind power ramp forecasting, reflecting the increasing interest on this topic observed since 2007. Three main aspects were identified from the literature: wind power ramp definition, ramp underlying meteorological causes and experi-ences in predicting ramps. In this framework, we additionally outline a number of recommendations and potential lines of research.