Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

Designens betydelse vid val av boxvin : En studie om konsumenters preferenser av designfaktorer på bag-in-box-vin.

På den svenska marknaden står bag-in-box-vin för över 50 procent av allt vin som säljs och Sverige är det land där bag-in-boxen fått starkast fäste i världen (Fremin, 2012). Det gör att bag-in-box-förpackningen är en stor och betydelsefull produkt för vintillverkare och att ha kunskap om säljande design är viktigt för grafiska formgivare samt övriga involverade företag. Den föreliggande studiens syfte var att undersöka vilka designfaktorer på bagin- box-förpackningar som påverkar konsumenternas val av vin i Sverige. Studien innefattade en visuell kartläggning som ämnade ge en överblick av utbudet på ett Systembolag. Detta genomfördes för att skapa en grund till den webbaserade enkätundersökningen som genomfördes under våren 2015. Resultaten visade att majoriteten av respondenterna valde vin baserat på förpackningens utseende och ansåg att de påverkades främst av färg, typsnitt, illustrationer och varumärken. Resultaten visade också att ju högre vinkunskap konsumenterna hade desto mindre påverkade designen deras val. Kvinnor och män hade generellt olika åsikter angående hur stor påverkan de olika designfaktorerna hade vid valet av bag-in-box-vin. Denna studie är endast ett avstamp till ett ämne som innefattar många påverkande faktorer. Det betyder att mer forskning krävs inom ämnet innan helt sanningsenliga resultat kan fastställas.

Understanding the "black box" of a health-promotion program : keys to enable health among older persons aging in the context of migration

Although the need to make health services more accessible to persons who have migrated has been identified, knowledge about health-promotion programs (HPPs) from the perspective of older persons born abroad is lacking. This study explores the design experiences and content implemented in an adapted version of a group-based HPP developed in a researcher-community partnership. Fourteen persons aged 70-83 years or older who had migrated to Sweden from Finland or the Balkan Peninsula were included. A grounded theory approach guided the data collection and analysis. The findings showed how participants and personnel jointly helped raise awareness. The participants experienced three key processes that could open doors to awareness: enabling community, providing opportunities to understand and be understood, and confirming human values and abilities. Depending on how the HPP content and design are being shaped by the group, the key processes could both inhibit or encourage opening doors to awareness. Therefore, this study provides key insights into how to enable health by deepening the understanding of how the exchange of health-promoting messages is experienced to be facilitated or hindered. This study adds to the scientific knowledge base of how the design and content of HPP may support and recognize the capabilities of persons aging in the context of migration.

Real-Time Flood Forecasting Systems In Flanders

As a highly urbanized and flood prone region, Flanders has experienced multiple floods causing significant damage in the past. In response to the floods of 1998 and 2002 the Flemish Environment Agency, responsible for managing 1 400 km of unnavigable rivers, started setting up a real time flood forecasting system in 2003. Currently the system covers almost 2 000 km of unnavigable rivers, for which flood forecasts are accessible online (www.waterinfo.be). The forecasting system comprises more than 1 000 hydrologic and 50 hydrodynamic models which are supplied with radar rainfall, rainfall forecasts and on-site observations. Forecasts for the next 2 days are generated hourly, while 10 day forecasts are generated twice a day. Additionally, twice daily simulations based on percentile rainfall forecasts (from EPS predictions) result in uncertainty bands for the latter. Subsequent flood forecasts use the most recent rainfall predictions and observed parameters at any time while uncertainty on the longer-term is taken into account. The flood forecasting system produces high resolution dynamic flood maps and graphs at about 200 river gauges and more than 3 000 forecast points. A customized emergency response system generates phone calls and text messages to a team of hydrologists initiating a pro-active response to prevent upcoming flood damage. The flood forecasting system of the Flemish Environment Agency is constantly evolving and has proven to be an indispensable tool in flood crisis management. This was clearly the case during the November 2010 floods, when the agency issued a press release 2 days in advance allowing water managers, emergency services and civilians to take measures.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

The role of no-arbitrage on forecasting: lessons from a parametric term structure model

Parametric term structure models have been successfully applied to innumerous problems in fixed income markets, including pricing, hedging, managing risk, as well as studying monetary policy implications. On their turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test if no-arbitrage affects forecasting. We construct cross section (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on U.S. Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and Root Mean Square Errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.

Tipificação molecular de amostras de Brucella spp. utilização a técnica de BOX-PCR

O gênero Brucella é formado por coco-bacilos Gram negativos patogênicos ao homem e animais, sendo classificado como patógeno de grupo de risco III. A identificação dessas bactérias apresenta várias limitações como: exigência de inoculação em vários meios, tempo de incubação longo e necessidade de soros imunes e bacteriófagos. Devido à sua alta patogenicidade e ao longo tempo de exposição dos laboratoristas à bactéria, a brucelose é uma das infecções mais freqüentemente adquiridas em laboratório. Além da contaminação em laboratório, a transmissão ao homem pode ocorrer através de animais infectados e ingestão de produtos derivados, como o leite cru. A procura de métodos rápidos de identificação das espécies e biovares pode ser útil para diminuir os riscos do manuseio desta bactéria e na tomada de medidas de controle epidemiológico. O principal objetivo deste trabalho foi facilitar a classificação de cepas de referência de Brucella spp. e isoladas no Brasil utilizando a técnica de rep-PCR com oligonucleotídeos do elemento BOX, uma seqüência repetida presente no genoma de várias bactérias. Foram analisados 38 isolados representando diferentes espécies e biovares de Brucella sp. e 13 isolados de gêneros relacionados como controle da especificidade da reação. Foi realizada uma confirmação prévia dos isolados de brucela por testes bioquímicos e PCR gênero-específica. A técnica de BOX-PCR agrupou todas as espécies e biovares de Brucella em um único grupo com nível de similaridade entre 100 e 74%. Diferenças entre os isolados, quanto a presença ou ausência de bandas, puderam ser observadas. Entretanto, essas divergências não caracterizam uma espécie ou biovar. Bandas comuns a todos os isolados de Brucella sp. podem caracterizar o gênero.

CCPFR : Collaborative Planning, Forecasting and Replenishment : é realmente possível a integração entre varejistas e fabricantes no processo de planejamento, na previsão de vendas e reabastecimento a ponto de diminuir as perdas nas vendas e aumentar a produtividade?

Trata da nova metodologia de planejamento colaborativo, previsão e reabastecimento, conhecida pela sigla CPFR. Aborda as principais lacunas das metodologias tradicionais, as oportunidades de negócios geradas, o modelo de negócios proposto pelo CPF R e suas etapas de implementação, as implicações sobre a organização, os principais problemas de implementação, metodologias e ferramentas de integração presentes nas empresas que utilizam o CPFR. Aponta oportunidades geradas pelo CPFR e características de integração presentes nas empresas que já utilizam o conceito.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.